{"id":3521,"date":"2023-08-31T11:12:58","date_gmt":"2023-08-31T11:12:58","guid":{"rendered":"https:\/\/neuraldesigner.com\/learning\/star-type\/"},"modified":"2026-08-06T15:45:45","modified_gmt":"2026-08-06T13:45:45","slug":"star-type","status":"publish","type":"learning","link":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/","title":{"rendered":"Star types classification using machine learning"},"content":{"rendered":"<style>\n.nds{width:100vw;margin-left:calc(50% - 50vw);padding:22px 24px 14px;background:#eee;color:#1b2635;font-family:\"Outfit\",\"Roboto\",Arial,sans-serif}\n.nds *{box-sizing:border-box}.nds-wrap{width:min(100%,1200px);margin:0 auto}.nds a{text-decoration:none;color:#2d799f}\n.nds-executive{margin:0 0 34px;padding:32px;border-radius:20px;background:linear-gradient(135deg,#12354b,#245e80);color:#fff}.nds-executive h2{margin:0 0 12px;color:#fff;font-size:30px}.nds-executive p{font-size:18px;line-height:1.55}\n.nds-kpis{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:14px;margin-top:22px}.nds-kpi{padding:18px;border:1px solid rgba(255,255,255,.18);border-radius:14px;background:rgba(255,255,255,.1)}.nds-kpi strong{display:block;font-size:25px}.nds-kpi span{font-size:13px}\n.nds-actions,.nds-audience,.nds-downloads{display:flex;flex-wrap:wrap;justify-content:center;gap:12px;margin:22px 0}.nds-actions a,.nds-downloads a{padding:12px 20px;border-radius:24px;background:#245e80;color:#fff;font-weight:700}.nds-executive .nds-actions a{background:#fff;color:#12354b}\n.nds-toc{display:flex;flex-wrap:wrap;justify-content:center;gap:10px;margin:0 0 42px;padding:0;list-style:none}.nds-toc a,.nds-audience span{display:block;padding:9px 14px;border-radius:20px;background:#e9f2f8;color:#12354b;font-weight:600}\n.nds-card{margin:0 0 54px;scroll-margin-top:90px}.nds-card h2{margin:0 0 20px;padding-bottom:12px;border-bottom:1px solid #dbe5ec;color:#001233;font-size:24px}.nds-card p,.nds-card li{font-size:16.5px;line-height:1.6}.nds-card img{display:block;width:auto;max-width:min(640px,100%);height:auto;margin:24px auto;border-radius:12px}.nds-card img.nds-architecture{width:min(1000px,100%);max-width:100%}\n.nds-value-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:16px;margin:22px 0}.nds-value{padding:20px;border:1px solid #dce8ef;border-radius:14px;background:#f8fbfd}.nds-note{margin:20px 0;padding:18px 20px;border-left:4px solid #56a1c8;border-radius:0 12px 12px 0;background:#f6fafc}.nds-note--warning{border-left-color:#d79a29;background:#fff9ed}\n.nds-table-scroll{max-width:100%;overflow-x:auto}.nds-card table{width:auto;max-width:100%;margin:24px auto;border-collapse:collapse;background:#fff}.nds-card th,.nds-card td{padding:11px 16px;border-bottom:1px solid #e2e9ee}.nds-card thead th{background:#12354b!important;color:#fff!important}.nds-card tbody th{background:#eaf2f6!important;color:#12354b!important;text-align:left}.nds-card tbody tr:nth-child(even) th{background:#f4f8fa!important}.nds-card tbody td{color:#33424f!important}\n.nds-figure-grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:20px}.nds-figure-grid figure,.nds-architecture-figure{margin:0;padding:16px;border:1px solid #dce8ef;border-radius:15px;background:#f8fbfd}.nds-figure-grid img{width:100%;max-width:100%;margin:0 auto 12px}.nds-card figcaption{color:#405361;font-size:14px;line-height:1.45}\n@media(max-width:820px){.nds-kpis{grid-template-columns:repeat(2,minmax(0,1fr))}.nds-value-grid{grid-template-columns:1fr}}\n@media(max-width:620px){.nds{padding:12px 14px}.nds-kpis,.nds-figure-grid{grid-template-columns:1fr}.nds-executive{padding:24px 20px}.nds-card table{font-size:13px}.nds-card th,.nds-card td{padding:9px 8px}}\n<\/style>\n<style>\n.nds code{padding:2px 5px;border-radius:4px;background:#e8eef2;color:#12354b}\n.nds-card h3{margin:27px 0 12px;color:#12354b;font-size:20px}\n.nds-card pre{margin:22px auto;padding:20px 22px;max-width:760px;overflow-x:auto;border:1px solid #dce5eb;border-radius:14px;background:#f7fafc!important;color:#173246!important;font:13px\/1.55 Consolas,Menlo,monospace}\n.nds-card pre code{padding:0;background:transparent;color:inherit}\n.nds-centered-figure{width:min(720px,100%);margin:24px auto;padding:16px;border:1px solid #dce8ef;border-radius:15px;background:#f8fbfd}\n.nds-centered-figure img{width:100%;max-width:100%;margin:0 auto 12px}\n.nds-flow{display:grid;grid-template-columns:repeat(6,minmax(0,1fr));gap:10px;margin:24px 0}\n.nds-flow div{position:relative;display:flex;min-height:96px;align-items:center;justify-content:center;padding:15px 10px;border-radius:13px;background:#12354b;color:#fff;text-align:center;font-weight:700}\n.nds-flow div:not(:last-child):after{content:\"\u2192\";position:absolute;right:-15px;top:50%;z-index:2;transform:translateY(-50%);color:#56a1c8;font-size:21px}\n.nds-metrics td:nth-child(2),.nds-confusion td{text-align:right;font-variant-numeric:tabular-nums}\n.nds-deployment-grid{display:grid;grid-template-columns:minmax(0,1.4fr) minmax(280px,1fr);gap:22px;align-items:center}\n.nds-result{padding:26px;border:1px solid #cfe1eb;border-radius:16px;background:#f7fbfd;text-align:center}\n.nds-result span,.nds-result strong{display:block}.nds-result strong{margin:8px 0;color:#12354b;font-size:30px}\n@media(max-width:1000px){.nds-flow{grid-template-columns:repeat(3,1fr)}.nds-flow div:after{display:none}}\n@media(max-width:760px){.nds-flow,.nds-deployment-grid{grid-template-columns:1fr}.nds-flow div:after{display:none}}<\/p>\n<p>.nds-calculator{margin:30px 0;padding:24px;border:1px solid #cfe1eb;border-radius:18px;background:#f7fbfd}\n.nds-calculator h3{margin-top:0}.nds-calculator-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:15px}\n.nds-calculator label{display:flex;flex-direction:column;gap:6px;color:#12354b;font-size:13px;font-weight:700}\n.nds-calculator input,.nds-calculator select{width:100%;min-height:46px;padding:10px 12px;border:1px solid #b9cfdb;border-radius:9px;background:#fff;color:#173246;font:inherit}\n.nds-calculator input:focus,.nds-calculator select:focus{outline:3px solid rgba(86,161,200,.22);border-color:#2d799f}\n.nds-calculator .is-invalid{border-color:#c94747;background:#fff8f8}\n.nds-calculator small{color:#617482;font-size:11px;font-weight:400}\n.nds-calculator-actions{display:flex;flex-wrap:wrap;justify-content:center;gap:10px;margin:20px 0}\n.nds-calculator button{padding:11px 19px;border:0;border-radius:24px;background:#245e80;color:#fff;font:inherit;font-weight:700;cursor:pointer}\n.nds-calculator button[type=reset]{background:#e1edf3;color:#12354b}\n.nds-calculator-error{min-height:24px;margin:0 0 10px;color:#a12828;text-align:center;font-weight:600}\n.nds-score-summary{margin:10px 0 18px;text-align:center}.nds-score-summary strong{display:block;color:#12354b;font-size:25px}\n.nds-score-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:12px}\n.nds-score{padding:13px;border:1px solid #d6e4eb;border-radius:12px;background:#fff}\n.nds-score-head{display:flex;justify-content:space-between;gap:8px;margin-bottom:8px;color:#173246}.nds-score-head strong{font-size:14px}.nds-score-head span{font-variant-numeric:tabular-nums}\n.nds-score-track{height:8px;overflow:hidden;border-radius:8px;background:#e8eff3}.nds-score-fill{height:100%;background:#56a1c8}\n.nds-score.is-winner{border-color:#2d799f;box-shadow:0 0 0 2px rgba(45,121,159,.12)}.nds-score.is-winner .nds-score-fill{background:#245e80}\n@media(max-width:820px){.nds-calculator-grid,.nds-score-grid{grid-template-columns:repeat(2,minmax(0,1fr))}}\n@media(max-width:560px){.nds-calculator-grid,.nds-score-grid{grid-template-columns:1fr}}\n<\/style>\n<div class=\"nds\" data-science-profile=\"physical-chemical\">\n<div class=\"nds-wrap\">\n<section class=\"nds-executive\">\n<h2>Classify six educational stellar categories from tabular properties<\/h2>\n<p>This reproducible tutorial expands six physical and categorical fields into 22 model features and assigns one of six labels with a fixed softmax classifier. The final export classifies 47 of 48 held-out rows correctly, with 97.9% accuracy and a macro-F1 of 0.974. These are internal results on a small curated data set.<\/p>\n<div class=\"nds-kpis\">\n<div class=\"nds-kpi\"><strong>97.9%<\/strong><span>held-out accuracy<\/span><\/div>\n<div class=\"nds-kpi\"><strong>0.974<\/strong><span>macro-F1<\/span><\/div>\n<div class=\"nds-kpi\"><strong>48<\/strong><span>held-out test rows<\/span><\/div>\n<div class=\"nds-kpi\"><strong>240<\/strong><span>total tabular records<\/span><\/div>\n<\/div>\n<div class=\"nds-actions\"><a href=\"#6-validation\">Review scientific validation<\/a><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/stars.csv\">Download the data<\/a><\/div>\n<\/section>\n<ul class=\"nds-toc\">\n<li><a href=\"#1-scientific-objective\">Scientific objective<\/a><\/li>\n<li><a href=\"#2-data-provenance\">Data and provenance<\/a><\/li>\n<li><a href=\"#3-model\">Model<\/a><\/li>\n<li><a href=\"#4-training\">Training<\/a><\/li>\n<li><a href=\"#5-selection\">Selection<\/a><\/li>\n<li><a href=\"#6-validation\">Validation<\/a><\/li>\n<li><a href=\"#7-inference\">Inference<\/a><\/li>\n<li><a href=\"#8-validity\">Validity<\/a><\/li>\n<\/ul>\n<section id=\"1-scientific-objective\" class=\"nds-card\">\n<h2>1. Scientific objective<\/h2>\n<p>The objective is to demonstrate multiclass classification from temperature, luminosity, radius, absolute magnitude, colour and spectral class. The output can support teaching, software checks and reproducible comparisons on this specific table. It is not a substitute for spectral analysis, catalogue classification or stellar-evolution inference.<\/p>\n<div class=\"nds-value-grid\">\n<div class=\"nds-value\"><strong>Multiclass workflow<\/strong><\/p>\n<p>Demonstrate categorical encoding, scaling, softmax classification and class-level evaluation in a compact example.<\/p>\n<\/div>\n<div class=\"nds-value\"><strong>HR-diagram intuition<\/strong><\/p>\n<p>Relate temperature, luminosity, radius and magnitude to visibly separated regions of an educational stellar table.<\/p>\n<\/div>\n<div class=\"nds-value\"><strong>Reproducible inference<\/strong><\/p>\n<p>Inspect the exact split, regenerated figures, executable Python export and Neural Designer project used for the reported result.<\/p>\n<\/div>\n<\/div>\n<div class=\"nds-audience\"><span>Astronomy education<\/span><span>Scientific data science<\/span><span>Research software<\/span><span>Introductory stellar astrophysics<\/span><\/div>\n<div class=\"nds-note\"><strong>Scope.<\/strong> This is a small educational benchmark. The labels combine substellar objects, stellar remnants, main-sequence subgroups and high-luminosity classes; they should not be treated as one authoritative astronomical taxonomy.<\/div>\n<\/section>\n<section id=\"2-data-provenance\" class=\"nds-card\">\n<h2>2. Data and provenance<\/h2>\n<p>The updated <a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/stars.csv\"><code>stars.csv<\/code><\/a> contains 240 complete and non-duplicated rows. Each of the six target labels occurs exactly 40 times, an intentionally balanced prevalence that is unlikely to represent an astronomical survey.<\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>CSV field<\/th>\n<th>Meaning in this data set<\/th>\n<th>Unit or encoding<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th><code>temperature<\/code><\/th>\n<td>Stellar effective\/surface temperature<\/td>\n<td>K<\/td>\n<\/tr>\n<tr>\n<th><code>luminosity<\/code><\/th>\n<td>Luminosity relative to the Sun<\/td>\n<td>L\/L<sub>\u2609<\/sub><\/td>\n<\/tr>\n<tr>\n<th><code>relative_radius<\/code><\/th>\n<td>Radius relative to the Sun<\/td>\n<td>R\/R<sub>\u2609<\/sub><\/td>\n<\/tr>\n<tr>\n<th><code>absolute_magnitude<\/code><\/th>\n<td>Absolute visual magnitude used by the source table<\/td>\n<td>M<sub>V<\/sub><\/td>\n<\/tr>\n<tr>\n<th><code>color<\/code><\/th>\n<td>Eleven qualitative colour categories<\/td>\n<td>nominal category<\/td>\n<\/tr>\n<tr>\n<th><code>spectral_class<\/code><\/th>\n<td>One of O, B, A, F, G, K or M<\/td>\n<td>nominal category<\/td>\n<\/tr>\n<tr>\n<th><code>type<\/code><\/th>\n<td>Target: Brown Dwarf, Hypergiants, Main Sequence, Red Dwarf, Supergiants or White Dwarf<\/td>\n<td>six-class category<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h3>Source fields and model features<\/h3>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Source field group<\/th>\n<th>Source columns<\/th>\n<th>Model features<\/th>\n<th>Preprocessing<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Physical values<\/th>\n<td>4<\/td>\n<td>4<\/td>\n<td>Mean-and-standard-deviation scaling<\/td>\n<\/tr>\n<tr>\n<th>Colour<\/th>\n<td>1<\/td>\n<td>11<\/td>\n<td>One-hot encoding and minimum\u2013maximum scaling<\/td>\n<\/tr>\n<tr>\n<th>Spectral class<\/th>\n<td>1<\/td>\n<td>7<\/td>\n<td>One-hot encoding and minimum\u2013maximum scaling<\/td>\n<\/tr>\n<tr>\n<th>Total<\/th>\n<td>6<\/td>\n<td>22<\/td>\n<td>Ordered schema required by the export<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Subset<\/th>\n<th>Rows<\/th>\n<th>Purpose<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Training<\/th>\n<td>144<\/td>\n<td>Estimate the fixed model parameters<\/td>\n<\/tr>\n<tr>\n<th>Selection<\/th>\n<td>48<\/td>\n<td>Monitor generalization during training<\/td>\n<\/tr>\n<tr>\n<th>Testing<\/th>\n<td>48<\/td>\n<td>Report final internal performance<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-figure-grid\">\n<figure><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/star-type-distribution-2026.png\" alt=\"Six target labels with forty records each\"><figcaption>The complete table is perfectly balanced, but the random 60\/20\/20 split does not preserve exactly equal testing counts.<\/figcaption><\/figure>\n<figure><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/star-type-correlations-2026.png\" alt=\"Pearson associations between source fields and a numerically encoded six-class target\"><figcaption>The target is nominal. These coefficients depend on its arbitrary numerical encoding and are not physical importance or causal measures.<\/figcaption><\/figure>\n<\/div>\n<div class=\"nds-note nds-note--provenance\"><strong>Provenance.<\/strong> The source is the Kaggle <a href=\"https:\/\/www.kaggle.com\/deepu1109\/star-dataset\">Star dataset to predict star types<\/a>. The repository does not document object identifiers, observing facilities, measurement uncertainties, acquisition dates, selection functions or a peer-reviewed construction protocol. The table should therefore be treated as a curated educational compilation, not a survey catalogue or externally validated astrophysical sample.<\/div>\n<\/section>\n<section id=\"3-model\" class=\"nds-card\">\n<h2>3. Model<\/h2>\n<p>The four numeric inputs are standardized. The eleven colour categories and seven spectral classes are one-hot encoded, producing 22 features. A single dense layer connects those features directly to six softmax outputs ordered as <code>Brown Dwarf<\/code>, <code>Hypergiants<\/code>, <code>Main Sequence<\/code>, <code>Red Dwarf<\/code>, <code>Supergiants<\/code> and <code>White Dwarf<\/code>.<\/p>\n<p>The model contains no hidden layer and has 138 trainable parameters. It is a multiclass logistic classifier represented in Neural Designer\u2019s neural-network framework.<\/p>\n<p>This is both the base and final architecture. No neuron-selection or architecture-selection experiment is used.<\/p>\n<div class=\"nds-note\"><strong>Output contract.<\/strong> The largest softmax score defines the predicted label. The scores have not been calibrated as probabilities and should not be interpreted as astrophysical confidence.<\/div>\n<figure class=\"nds-architecture-figure\" data-model-stage=\"initial\"><img decoding=\"async\" class=\"nds-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/star-type-network-architecture-2026.png\" alt=\"Fixed star-type model with six source fields, twenty-two encoded features and six softmax outputs\"><figcaption>Fixed base and final architecture. The diagram shows the six source fields; colour and spectral class expand internally so the dense softmax layer receives 22 features.<\/figcaption><\/figure>\n<\/section>\n<section id=\"4-training\" class=\"nds-card\">\n<h2>4. Training strategy<\/h2>\n<p>The model minimizes multiclass cross-entropy with the quasi-Newton method and no explicit regularization. Training stopped at the configured loss goal after 20 completed epochs (21 stored iterations). Training cross-entropy decreased from 1.8483 to 0.0006.<\/p>\n<p>Selection cross-entropy fell from 1.2338 to its minimum of 0.342 at epoch 7, then increased to 0.6664 while training error continued to fall. The downloadable export is the stored final model at epoch 20; the earlier checkpoint was not restored.<\/p>\n<figure class=\"nds-centered-figure\"><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/star-type-training-history-2026.png\" alt=\"Training and selection cross-entropy histories over twenty epochs\"><figcaption>The divergence after epoch 7 is a clear overfitting signal despite the strong held-out classification result.<\/figcaption><\/figure>\n<\/section>\n<section id=\"5-selection\" class=\"nds-card\">\n<h2>5. Model selection and baseline<\/h2>\n<p><strong>No neuron selection was performed, and no architecture selection was performed.<\/strong> The direct 22\u20136 softmax model was fixed before training and retained as the final model. This demonstrates that a larger network is unnecessary for separating this small curated table, but it does not establish that the architecture is optimal.<\/p>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Reference<\/th>\n<th>Expected\/test accuracy<\/th>\n<th>Interpretation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Uniform six-class guess<\/th>\n<td>16.7% expected<\/td>\n<td>Chance reference under the deliberately balanced full data set<\/td>\n<\/tr>\n<tr>\n<th>Fixed softmax model<\/th>\n<td>97.9% testing<\/td>\n<td>Strong internal separation of the curated labels<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-note\"><strong>Training recommendation.<\/strong> Architecture selection is not needed here, but checkpoint selection is. A production-quality rerun should stop near the lowest selection cross-entropy and preserve that checkpoint before testing.<\/div>\n<\/section>\n<section id=\"6-validation\" class=\"nds-card\">\n<h2>6. Scientific validation<\/h2>\n<p>The random testing subset contains 48 rows. The final exported model correctly classifies 47; one Supergiants row is assigned to Main Sequence. Because every class has only 4\u201312 testing examples, each error changes the class-level metrics substantially.<\/p>\n<div class=\"nds-table-scroll\">\n<table class=\"nds-metrics\">\n<thead>\n<tr>\n<th>Testing metric<\/th>\n<th>Value<\/th>\n<th>Interpretation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Accuracy<\/th>\n<td>97.92%<\/td>\n<td>47 correct labels from 48 testing rows<\/td>\n<\/tr>\n<tr>\n<th>Macro precision<\/th>\n<td>96.67%<\/td>\n<td>Unweighted mean across the six labels<\/td>\n<\/tr>\n<tr>\n<th>Macro recall<\/th>\n<td>98.61%<\/td>\n<td>Unweighted mean sensitivity across labels<\/td>\n<\/tr>\n<tr>\n<th>Macro-F1<\/th>\n<td>97.42%<\/td>\n<td>Class-balanced summary of precision and recall<\/td>\n<\/tr>\n<tr>\n<th>Testing cross-entropy<\/th>\n<td>0.322<\/td>\n<td>Calculated from the exact final Python export<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h3>Confusion matrix<\/h3>\n<div class=\"nds-table-scroll\">\n<table class=\"nds-confusion\">\n<thead>\n<tr>\n<th>Actual \/ predicted<\/th>\n<th>Brown dwarf<\/th>\n<th>Hypergiants<\/th>\n<th>Main sequence<\/th>\n<th>Red dwarf<\/th>\n<th>Supergiants<\/th>\n<th>White dwarf<\/th>\n<th>Total<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Brown dwarf<\/th>\n<td>9<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>9<\/td>\n<\/tr>\n<tr>\n<th>Hypergiants<\/th>\n<td>0<\/td>\n<td>9<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>9<\/td>\n<\/tr>\n<tr>\n<th>Main sequence<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>4<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>4<\/td>\n<\/tr>\n<tr>\n<th>Red dwarf<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>7<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>7<\/td>\n<\/tr>\n<tr>\n<th>Supergiants<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>1<\/td>\n<td>0<\/td>\n<td>11<\/td>\n<td>0<\/td>\n<td>12<\/td>\n<\/tr>\n<tr>\n<th>White dwarf<\/th>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>0<\/td>\n<td>7<\/td>\n<td>7<\/td>\n<\/tr>\n<tr>\n<th>Total<\/th>\n<td>9<\/td>\n<td>9<\/td>\n<td>5<\/td>\n<td>7<\/td>\n<td>11<\/td>\n<td>7<\/td>\n<td>48<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h3>Per-class performance<\/h3>\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Label<\/th>\n<th>Testing rows<\/th>\n<th>Precision<\/th>\n<th>Recall<\/th>\n<th>F1<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Brown dwarf<\/th>\n<td>9<\/td>\n<td>100.0%<\/td>\n<td>100.0%<\/td>\n<td>100.0%<\/td>\n<\/tr>\n<tr>\n<th>Hypergiants<\/th>\n<td>9<\/td>\n<td>100.0%<\/td>\n<td>100.0%<\/td>\n<td>100.0%<\/td>\n<\/tr>\n<tr>\n<th>Main sequence<\/th>\n<td>4<\/td>\n<td>80.0%<\/td>\n<td>100.0%<\/td>\n<td>88.9%<\/td>\n<\/tr>\n<tr>\n<th>Red dwarf<\/th>\n<td>7<\/td>\n<td>100.0%<\/td>\n<td>100.0%<\/td>\n<td>100.0%<\/td>\n<\/tr>\n<tr>\n<th>Supergiants<\/th>\n<td>12<\/td>\n<td>100.0%<\/td>\n<td>91.7%<\/td>\n<td>95.7%<\/td>\n<\/tr>\n<tr>\n<th>White dwarf<\/th>\n<td>7<\/td>\n<td>100.0%<\/td>\n<td>100.0%<\/td>\n<td>100.0%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-figure-grid\"><\/div>\n<div class=\"nds-note\"><strong>Scientific interpretation.<\/strong> The result shows that the supplied labels are highly separable within this table. It does not demonstrate transfer to Gaia, spectroscopic surveys or new stellar populations, and the 48-row test set is too small to characterize rare failure modes or calibration.<\/div>\n<\/section>\n<section id=\"7-inference\" class=\"nds-card\">\n<h2>7. Inference and reproducibility<\/h2>\n<p>A credible application would begin with traceable photometric or spectroscopic measurements, apply quality and uncertainty filters, derive physically documented quantities, validate the six-field schema, encode the two categorical fields, calculate six model scores and submit the result to astronomical review.<\/p>\n<div class=\"nds-flow\">\n<div>Instrument or catalogue<\/div>\n<div>Quality and uncertainty checks<\/div>\n<div>Physical and spectral fields<\/div>\n<div>22-feature encoding<\/div>\n<div>Six model scores<\/div>\n<div>Astronomical review<\/div>\n<\/div>\n<h3>Held-out white-dwarf example<\/h3>\n<p>The following record belongs to the testing subset and is reproduced with the exact Python export. It replaces the previous artificial input combination with one internally consistent row from the published table.<\/p>\n<div class=\"nds-deployment-grid\">\n<div class=\"nds-table-scroll\">\n<table>\n<thead>\n<tr>\n<th>Input<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<th>Temperature<\/th>\n<td>9,675 K<\/td>\n<\/tr>\n<tr>\n<th>Luminosity<\/th>\n<td>0.00045 L\/L<sub>\u2609<\/sub><\/td>\n<\/tr>\n<tr>\n<th>Relative radius<\/th>\n<td>0.0109 R\/R<sub>\u2609<\/sub><\/td>\n<\/tr>\n<tr>\n<th>Absolute magnitude<\/th>\n<td>13.98 M<sub>V<\/sub><\/td>\n<\/tr>\n<tr>\n<th>Colour<\/th>\n<td>Blue-White<\/td>\n<\/tr>\n<tr>\n<th>Spectral class<\/th>\n<td>A<\/td>\n<\/tr>\n<tr>\n<th>Recorded target<\/th>\n<td>White Dwarf<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class=\"nds-result\"><span>Highest model score<\/span><strong>White Dwarf<\/strong><\/p>\n<p>White Dwarf 69.34%; Main Sequence 30.65%; every other score is below 0.01%. The predicted label matches the stored testing target, but the score margin also shows why the output should not be presented as certainty.<\/p>\n<\/div>\n<\/div>\n<div id=\"nds-stars-calculator\" class=\"nds-calculator\">\n<h3>Try the exported stellar classifier<\/h3>\n<p>Provide the four numeric fields and select the qualitative colour and spectral class. The browser reproduces the 22-feature encoding used by Neural Designer.<\/p>\n<div class=\"nds-note\"><strong>Research demonstration.<\/strong> The calculation runs locally with the exact exported weights and preprocessing. Values outside the validated domain are rejected. Scores are not calibrated probabilities or an astronomical diagnosis.<\/div>\n<form novalidate>\n<div class=\"nds-calculator-grid\"><label for=\"nds-stars-temperature\">Temperature<input id=\"nds-stars-temperature\" name=\"temperature\" type=\"text\" inputmode=\"decimal\" value=\"9675\" autocomplete=\"off\"><small>1,939\u201340,000 K<\/small><\/label><label for=\"nds-stars-luminosity\">Relative luminosity<input id=\"nds-stars-luminosity\" name=\"luminosity\" type=\"text\" inputmode=\"decimal\" value=\"0.00045\" autocomplete=\"off\"><small>0.00008\u2013849,420 L\/L\u2609<\/small><\/label><label for=\"nds-stars-relative_radius\">Relative radius<input id=\"nds-stars-relative_radius\" name=\"relative_radius\" type=\"text\" inputmode=\"decimal\" value=\"0.0109\" autocomplete=\"off\"><small>0.0084\u20131,948.5 R\/R\u2609<\/small><\/label><label for=\"nds-stars-absolute_magnitude\">Absolute magnitude<input id=\"nds-stars-absolute_magnitude\" name=\"absolute_magnitude\" type=\"text\" inputmode=\"decimal\" value=\"13.98\" autocomplete=\"off\"><small>\u221211.92\u201320.06 Mv<\/small><\/label><label for=\"nds-stars-color\">Colour<select id=\"nds-stars-color\" name=\"color\"><option>Blue<\/option><option selected>Blue-White<\/option><option>Orange<\/option><option>Orange-Red<\/option><option>Pale Yellow-Orange<\/option><option>Red<\/option><option>White<\/option><option>Whitish<\/option><option>Yellow-White<\/option><option>Yellowish<\/option><option>Yellowish-White<\/option><\/select><small>Category present in the dataset<\/small><\/label><label for=\"nds-stars-spectral_class\">Spectral class<select id=\"nds-stars-spectral_class\" name=\"spectral_class\"><option selected>A<\/option><option>B<\/option><option>F<\/option><option>G<\/option><option>K<\/option><option>M<\/option><option>O<\/option><\/select><small>O, B, A, F, G, K or M<\/small><\/label><\/div>\n<div class=\"nds-calculator-actions\"><button type=\"submit\">Calculate type scores<\/button><button type=\"reset\">Reset example<\/button><\/div>\n<p class=\"nds-calculator-error\" role=\"alert\">\n<\/form>\n<div class=\"nds-score-summary\" aria-live=\"polite\"><\/div>\n<div class=\"nds-score-grid\"><\/div>\n<\/div>\n<h3>Reproduce the inference<\/h3>\n<p>The Python package contains the exact exported model, the 22-feature encoding schema, the held-out case and its expected scores. The project package preserves the split, trained parameters and regenerated analyses.<\/p>\n<pre><code>from model import NeuralNetwork\n\n# Four numeric values + 11 colour indicators + 7 spectral indicators\nscores = NeuralNetwork().calculate_outputs(encoded_inputs)<\/code><\/pre>\n<div class=\"nds-downloads\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/star-type-python-model-2026.zip\">Download Python model (ZIP)<\/a><br \/>\n<a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/star-type-neural-designer-project-2026.zip\">Download Neural Designer project (ZIP)<\/a><br \/>\n<a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/08\/stars.csv\">Download stars.csv<\/a><\/div>\n<\/section>\n<section id=\"8-validity\" class=\"nds-card\">\n<h2>8. Validity, uncertainty and limitations<\/h2>\n<ul>\n<li><strong>External provenance is incomplete.<\/strong> The source page does not identify the stars, instruments, surveys, measurement methods or uncertainties behind the 240 rows.<\/li>\n<li><strong>The taxonomy mixes different concepts.<\/strong> Brown dwarfs are substellar objects; white dwarfs are remnants; red dwarfs are normally main-sequence stars; and supergiant\/hypergiant terminology describes luminosity or evolutionary state.<\/li>\n<li><strong>The class balance is artificial.<\/strong> Exactly 40 rows per target is useful for teaching but does not represent astronomical prevalence.<\/li>\n<li><strong>Inputs are physically redundant.<\/strong> Temperature, luminosity, radius, magnitude, colour and spectral class are related. Strong performance partly reflects repeated information rather than independent evidence.<\/li>\n<li><strong>The random row split is only internal validation.<\/strong> It does not test transfer across instruments, surveys, sky regions, extinction regimes or observing conditions.<\/li>\n<li><strong>Training overfits after epoch 7.<\/strong> The published final export reaches lower training loss but higher selection loss than the best stored epoch.<\/li>\n<li><strong>The test set is small.<\/strong> With 4\u201312 rows per label, confidence intervals would be wide and one mistake materially changes a class metric.<\/li>\n<li><strong>Scores are not calibrated.<\/strong> They are ranking values for this model and data set, not posterior class probabilities.<\/li>\n<\/ul>\n<\/section>\n<section id=\"references\" class=\"nds-card\">\n<h2>References<\/h2>\n<ul>\n<li>D. Baidya, <a href=\"https:\/\/www.kaggle.com\/deepu1109\/star-dataset\">Star dataset to predict star types<\/a>, Kaggle, accessed August 2026.<\/li>\n<li>ESA\/Gaia, <a href=\"https:\/\/sci.esa.int\/web\/gaia\/-\/60240-the-hertzsprung-russell-diagram\">The Hertzsprung\u2013Russell diagram<\/a>.<\/li>\n<li>ESA\/Gaia, <a href=\"https:\/\/www.cosmos.esa.int\/web\/gaia\/gaiadr2_hrd\">Gaia Data Release 2 Hertzsprung\u2013Russell diagrams<\/a>.<\/li>\n<li>NASA Science, <a href=\"https:\/\/science.nasa.gov\/exoplanets\/stars\/\">Stars and the O\u2013B\u2013A\u2013F\u2013G\u2013K\u2013M main-sequence classification<\/a>.<\/li>\n<\/ul>\n<\/section>\n<\/div>\n<\/div>\n<p><script data-noptimize=\"1\"> (() => { const root = document.getElementById(\"nds-stars-calculator\"); if (!root || root.dataset.ready === \"1\") return; root.dataset.ready = \"1\"; const config = {\"scalers\":[{\"kind\":\"standard\",\"mean\":10497.5,\"scale\":9532.5},{\"kind\":\"standard\",\"mean\":107188.0,\"scale\":179058.0},{\"kind\":\"standard\",\"mean\":237.1580048,\"scale\":516.0770264},{\"kind\":\"standard\",\"mean\":4.382400036,\"scale\":10.51049995},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"},{\"kind\":\"binary_minmax\"}],\"biases\":[-0.2117777616,-0.09377803653,0.3196601272,-0.3949817419,0.1171163768,0.263761282],\"weights\":[[-0.8493922949,-0.9271668792,-0.685033381,0.6182791591,-0.3075172007,-0.4259456396,0.2376400828,-0.09285766631,0.2046545893,1.583975196,0.02795274742,-0.0907657221,-0.005702391267,0.07824982703,0.2217487991,-0.07663747668,-0.3019866049,-0.2064545006,0.1190630347,0.1084838957,1.670671582,-0.1965401024],[0.6232122183,1.69681108,4.337046623,-2.237378836,-0.1300671101,0.1866831779,0.3591446877,-0.02774120122,0.05382310972,0.04949129373,0.005943047814,-0.07017720491,-0.133859545,0.0702527687,-0.01472780295,-0.1996146441,0.5622336864,-0.2646120191,0.2048298866,0.3087375164,0.003303784877,-0.05631083995],[1.745537519,-0.8990246654,-1.18709743,-1.807364225,-0.5916153193,0.9822847843,-0.2675429583,0.06982586533,-0.3725505471,-1.617037296,-0.7843466401,-0.08057364821,0.9056725502,-0.02289744467,-0.3970428109,0.563011229,-0.4164426923,0.5421835184,-0.3454048336,-0.04137485847,-1.663695931,-0.1195771992],[-0.9475577474,-0.8262633681,-0.6825406551,3.178287983,-0.1259561181,-0.3549281955,0.3576470912,-0.05761513487,0.3882826567,1.520690203,0.1175922081,0.09881370515,0.1452810466,0.2678664923,0.262675792,0.07370074093,-0.2144297361,-0.07903068513,0.4206066132,0.3607363701,1.684761643,-0.005055248737],[-1.040218115,1.926738858,-0.8880441785,-1.90762341,1.091370225,-0.8020739555,-0.1360710859,0.08679864556,-0.09772824496,0.2411085665,-0.3068903983,0.06469156593,-0.2894529104,-0.1996114254,-0.2041354775,-0.4414972663,-0.8659387827,-0.565692544,-0.05766151473,-0.2798145413,0.3391071856,1.332666159],[0.6257887483,-1.027156711,-0.7022093534,2.509346962,0.1533496827,0.2612586319,-0.2333250344,0.03627190739,-0.01773130521,-1.954874396,0.9399613738,0.0643806532,-0.5536043048,-0.4229682088,-0.01972300932,0.1452764869,1.471416116,0.5445854068,-0.265271157,-0.486333698,-1.815253973,-0.7619716525]],\"classes\":[\"Brown Dwarf\",\"Hypergiants\",\"Main Sequence\",\"Red Dwarf\",\"Supergiants\",\"White Dwarf\"],\"fields\":[{\"name\":\"temperature\",\"label\":\"Temperature\",\"kind\":\"number\",\"min\":1939,\"max\":40000,\"minLabel\":\"1,939\",\"maxLabel\":\"40,000\"},{\"name\":\"luminosity\",\"label\":\"Relative luminosity\",\"kind\":\"number\",\"min\":8e-05,\"max\":849420,\"minLabel\":\"0.00008\",\"maxLabel\":\"849,420\"},{\"name\":\"relative_radius\",\"label\":\"Relative radius\",\"kind\":\"number\",\"min\":0.0084,\"max\":1948.5,\"minLabel\":\"0.0084\",\"maxLabel\":\"1,948.5\"},{\"name\":\"absolute_magnitude\",\"label\":\"Absolute magnitude\",\"kind\":\"number\",\"min\":-11.92,\"max\":20.06,\"minLabel\":\"\u221211.92\",\"maxLabel\":\"20.06\"},{\"name\":\"color\",\"label\":\"Colour\",\"kind\":\"select\"},{\"name\":\"spectral_class\",\"label\":\"Spectral class\",\"kind\":\"select\"}],\"encoder\":\"stars\",\"predictionNoun\":\"Predicted label\"}; const form = root.querySelector(\"form\"); const error = root.querySelector(\".nds-calculator-error\"); const summary = root.querySelector(\".nds-score-summary\"); const scoreGrid = root.querySelector(\".nds-score-grid\");  const parseDecimal = value => { const normalized = String(value).trim().replace(\",\", \".\"); return normalized === \"\" ? NaN : Number(normalized); }; const softmax = logits => { const maximum = Math.max(...logits); const values = logits.map(value => Math.exp(value - maximum)); const total = values.reduce((sum, value) => sum + value, 0); return values.map(value => value \/ total); }; const modelScores = inputs => { const scaled = inputs.map((value, index) => { const scaler = config.scalers[index]; return scaler.kind === \"standard\" ? (value - scaler.mean) \/ scaler.scale : 2 * value - 1; }); const logits = config.biases.map((bias, row) => bias + config.weights[row].reduce( (sum, weight, column) => sum + weight * scaled[column], 0 ) ); return softmax(logits); }; const readValues = () => { root.querySelectorAll(\".is-invalid\").forEach( element => element.classList.remove(\"is-invalid\") ); const raw = {}; for (const field of config.fields) { const element = form.elements[field.name]; if (field.kind === \"select\") { raw[field.name] = element.value; continue; } const value = parseDecimal(element.value); if (!Number.isFinite(value) || value < field.min || value > field.max) { element.classList.add(\"is-invalid\"); throw new Error( `${field.label} must be between ${field.minLabel} and ${field.maxLabel}.` ); } raw[field.name] = value; } if (config.encoder === \"stars\") { const colors = [\"Blue\",\"Blue-White\",\"Orange\",\"Orange-Red\", \"Pale Yellow-Orange\",\"Red\",\"White\",\"Whitish\",\"Yellow-White\", \"Yellowish\",\"Yellowish-White\"]; const spectra = [\"A\",\"B\",\"F\",\"G\",\"K\",\"M\",\"O\"]; return [raw.temperature, raw.luminosity, raw.relative_radius, raw.absolute_magnitude, ...colors.map(value => Number(raw.color === value)), ...spectra.map(value => Number(raw.spectral_class === value))]; } return config.fields.map(field => raw[field.name]); }; const render = scores => { const winner = scores.indexOf(Math.max(...scores)); summary.innerHTML = `\\x3cspan>Highest model score\\x3c\/span>\\x3cstrong>${ config.predictionNoun}: ${config.classes[winner]}\\x3c\/strong>`; scoreGrid.innerHTML = scores.map((score, index) => ` \\x3cdiv class=\"nds-score ${index === winner ? \"is-winner\" : \"\"}\"> \\x3cdiv class=\"nds-score-head\">\\x3cstrong>${config.classes[index]}\\x3c\/strong> \\x3cspan>${(100 * score).toFixed(score < 0.0001 ? 4 : 2)}%\\x3c\/span>\\x3c\/div> \\x3cdiv class=\"nds-score-track\">\\x3cdiv class=\"nds-score-fill\" style=\"width:${Math.max(0.15, 100 * score)}%\">\\x3c\/div>\\x3c\/div> \\x3c\/div>`).join(\"\"); }; const calculate = () => { try { error.textContent = \"\"; render(modelScores(readValues())); } catch (exception) { error.textContent = exception.message; summary.innerHTML = \"\"; scoreGrid.innerHTML = \"\"; } }; form.addEventListener(\"submit\", event => { event.preventDefault(); calculate(); }); form.addEventListener(\"reset\", () => setTimeout(calculate, 0)); calculate(); })(); <\/script><\/p>\n","protected":false},"author":13,"featured_media":1490,"template":"","categories":[29],"tags":[],"class_list":["post-3521","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Star types classification using machine learning - Neural Designer<\/title>\n<meta name=\"description\" content=\"Build a machine learning model for star classification based on luminosity, radius, color, and other stellar characteristics.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Star types machine learning example\" \/>\n<meta property=\"og:description\" content=\"The main goal here is to design a model that makes proper classifications for the different star types.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/\" \/>\n<meta property=\"og:site_name\" content=\"Neural Designer\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-06T13:45:45+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Star types machine learning example\" \/>\n<meta name=\"twitter:description\" content=\"The main goal here is to design a model that makes proper classifications for the different star types.\" \/>\n<meta name=\"twitter:image\" content=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp\" \/>\n<meta name=\"twitter:site\" content=\"@NeuralDesigner\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/\",\"url\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/\",\"name\":\"Star types classification using machine learning - Neural Designer\",\"isPartOf\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp\",\"datePublished\":\"2023-08-31T11:12:58+00:00\",\"dateModified\":\"2026-08-06T13:45:45+00:00\",\"description\":\"Build a machine learning model for star classification based on luminosity, radius, color, and other stellar characteristics.\",\"breadcrumb\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#primaryimage\",\"url\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp\",\"contentUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp\",\"width\":1200,\"height\":628,\"caption\":\"Star types\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.neuraldesigner.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning\",\"item\":\"https:\/\/www.neuraldesigner.com\/learning\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Star types classification using machine learning\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#website\",\"url\":\"https:\/\/www.neuraldesigner.com\/\",\"name\":\"Neural Designer\",\"description\":\"Explainable AI Platform\",\"publisher\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.neuraldesigner.com\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#organization\",\"name\":\"Neural Designer\",\"url\":\"https:\/\/www.neuraldesigner.com\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png\",\"contentUrl\":\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png\",\"width\":1024,\"height\":223,\"caption\":\"Neural Designer\"},\"image\":{\"@id\":\"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/x.com\/NeuralDesigner\",\"https:\/\/es.linkedin.com\/showcase\/neuraldesigner\/\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Star types classification using machine learning - Neural Designer","description":"Build a machine learning model for star classification based on luminosity, radius, color, and other stellar characteristics.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/","og_locale":"en_US","og_type":"article","og_title":"Star types machine learning example","og_description":"The main goal here is to design a model that makes proper classifications for the different star types.","og_url":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/","og_site_name":"Neural Designer","article_modified_time":"2026-08-06T13:45:45+00:00","og_image":[{"width":1200,"height":628,"url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp","type":"image\/webp"}],"twitter_card":"summary_large_image","twitter_title":"Star types machine learning example","twitter_description":"The main goal here is to design a model that makes proper classifications for the different star types.","twitter_image":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp","twitter_site":"@NeuralDesigner","twitter_misc":{"Est. reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/","url":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/","name":"Star types classification using machine learning - Neural Designer","isPartOf":{"@id":"https:\/\/www.neuraldesigner.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#primaryimage"},"image":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#primaryimage"},"thumbnailUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp","datePublished":"2023-08-31T11:12:58+00:00","dateModified":"2026-08-06T13:45:45+00:00","description":"Build a machine learning model for star classification based on luminosity, radius, color, and other stellar characteristics.","breadcrumb":{"@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#primaryimage","url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp","contentUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/06\/star-types.webp","width":1200,"height":628,"caption":"Star types"},{"@type":"BreadcrumbList","@id":"https:\/\/www.neuraldesigner.com\/learning\/examples\/star-type\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.neuraldesigner.com\/"},{"@type":"ListItem","position":2,"name":"Learning","item":"https:\/\/www.neuraldesigner.com\/learning\/"},{"@type":"ListItem","position":3,"name":"Star types classification using machine learning"}]},{"@type":"WebSite","@id":"https:\/\/www.neuraldesigner.com\/#website","url":"https:\/\/www.neuraldesigner.com\/","name":"Neural Designer","description":"Explainable AI Platform","publisher":{"@id":"https:\/\/www.neuraldesigner.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.neuraldesigner.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.neuraldesigner.com\/#organization","name":"Neural Designer","url":"https:\/\/www.neuraldesigner.com\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/","url":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png","contentUrl":"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2023\/05\/logo-neural-1.png","width":1024,"height":223,"caption":"Neural Designer"},"image":{"@id":"https:\/\/www.neuraldesigner.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/NeuralDesigner","https:\/\/es.linkedin.com\/showcase\/neuraldesigner\/"]}]}},"_links":{"self":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning"}],"about":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/types\/learning"}],"author":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/users\/13"}],"version-history":[{"count":7,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3521\/revisions"}],"predecessor-version":[{"id":23145,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/learning\/3521\/revisions\/23145"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/media\/1490"}],"wp:attachment":[{"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/media?parent=3521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/categories?post=3521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.neuraldesigner.com\/api\/wp\/v2\/tags?post=3521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}