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background-size:cover!important;background-position:center!important; color:#193645; }\/* nd-shared-components:end *\/\n<\/style>\n<div class=\"ndb\">\n<div class=\"ndb-wrap\">\n<section class=\"ndb-executive\">\n<h2>Exploring patterns in employee attrition<\/h2>\n<p>Workforce analytics can describe where retention questions merit further investigation. This example models recorded attrition from the supplied employee attributes and evaluates the resulting scores on held-out records.<\/p>\n<div class=\"ndb-kpis\">\n<div class=\"ndb-kpi\"><strong>1,470<\/strong><span>Source records<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>48<\/strong><span>Final input features<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>294<\/strong><span>Test observations<\/span><\/div>\n<div class=\"ndb-kpi\"><strong>0.818<\/strong><span>Test ROC AUC<\/span><\/div>\n<\/div>\n<div class=\"ndb-actions\"><a class=\"aui aui-button\" href=\"#7-model-deployment\">Explore the model<\/a><\/div>\n<\/section>\n<ul class=\"ndb-toc\">\n<li><a href=\"#1-industrial-challenge\">Business decision<\/a><\/li><li><a href=\"#2-data-set\">Data set<\/a><\/li><li><a href=\"#3-model\">Model<\/a><\/li><li><a href=\"#4-training\">Training<\/a><\/li><li><a href=\"#5-selection\">Model selection<\/a><\/li><li><a href=\"#6-testing\">Testing<\/a><\/li><li><a href=\"#7-model-deployment\">Deployment<\/a><\/li><li><a href=\"#8-limitations\">Limitations<\/a><\/li>\n<\/ul>\n<span id=\"ApplicationType\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><span id=\"Contents\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"1-industrial-challenge\" class=\"ndb-card\"><h2>1. Business decision<\/h2><p>Workforce planning benefits from understanding patterns in historical departures. A predictive association does not explain why an employee leaves or identify which intervention would change that outcome.<\/p><div class=\"ndb-value-grid\"><div class=\"ndb-value\"><h3>Historical patterns<\/h3><p>Study the recorded attrition=Yes outcome.<\/p><\/div><div class=\"ndb-value\"><h3>Planning context<\/h3><p>Interpret results alongside organizational and employment context.<\/p><\/div><div class=\"ndb-value\"><h3>Intervention evidence<\/h3><p>Test proposed retention measures separately from the classifier.<\/p><\/div><\/div><div class=\"ndb-audience\"><span>People analytics<\/span><span>Organizational research<\/span><span>HR planning<\/span><\/div><div class=\"ndb-note\">Do not use benchmark scores alone for employment decisions. Establish dataset provenance and representativeness, review privacy and sensitive attributes, validate across time and organizational units, and investigate group-level findings with human review. The model is not an estimate of an intervention&#x27;s effect.<\/div><\/section>\n<span id=\"DataSet\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><span id=\"DataSetDetails\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"2-data-set\" class=\"ndb-card\"><h2>2. Data set<\/h2><p>The supplied benchmark contains 1,470 employee records. The endpoint is attrition=Yes. The project provides a retrospective modelling exercise; it does not demonstrate that a particular workplace policy caused departures or will prevent them.<\/p><p>The saved Neural Designer project and report define the exact version used here. The project does not identify an independently verified external data record. <\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Subset<\/th><th scope=\"col\">Records<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Training<\/th><td>882<\/td><\/tr><tr><th scope=\"row\">Validation \/ selection<\/th><td>294<\/td><\/tr><tr><th scope=\"row\">Testing<\/th><td>294<\/td><\/tr><tr><th scope=\"row\">Unused<\/th><td>0<\/td><\/tr><\/tbody><\/table><\/div><details><summary>Inspect the full variable schema<\/summary><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Variable<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Type<\/th><th scope=\"col\">Encoding<\/th><th scope=\"col\">Unit<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">age<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">business_travel<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">daily_rate<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">department<\/th><td>Input<\/td><td>Categorical<\/td><td>Human Resources; Research &amp; Development; Sales<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">distance_from_home<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">education<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">education_field<\/th><td>Input<\/td><td>Categorical<\/td><td>Human Resources; Life Sciences; Marketing; Medical; Other; Technical Degree<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">employee_count<\/th><td>None<\/td><td>Constant<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">employee_number<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">environment_satisfaction<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">gender<\/th><td>Input<\/td><td>Binary<\/td><td>Female; Male<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">hourly_rate<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">job_involvement<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">job_level<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">job_role<\/th><td>Input<\/td><td>Categorical<\/td><td>9 categories; see schema<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">job_satisfaction<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">marital_status<\/th><td>Input<\/td><td>Categorical<\/td><td>Divorced; Married; Single<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">monthly_income<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">monthly_rate<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">num_companies_worked<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">over_18<\/th><td>None<\/td><td>Constant<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">over_time<\/th><td>Input<\/td><td>Binary<\/td><td>No; Yes<\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">percent_salary_hike<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">performance_rating<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">relationship_satisfaction<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">standard_hours<\/th><td>None<\/td><td>Constant<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">stock_option_level<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">total_working_years<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">training_times_last_year<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">work_life_balance<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">years_at_company<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">years_in_current_role<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">years_since_last_promotion<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">years_with_curr_manager<\/th><td>Input<\/td><td>Numeric<\/td><td><\/td><td>As supplied<\/td><\/tr><tr><th scope=\"row\">attrition<\/th><td>Target<\/td><td>Binary<\/td><td>No; Yes<\/td><td>As supplied<\/td><\/tr><\/tbody><\/table><\/div><\/details><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-employee-attrition-t0-s38-20260921\"><div class=\"nd-chart-host\" id=\"nd-employee-attrition-t0-s38-20260921\" role=\"img\" aria-label=\"attrition pie chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: attrition pie chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"direction\":\"clockwise\",\"hovertemplate\":\"%{label}\\u003cbr\\u003e%{value}\\u003cbr\\u003e%{percent}\\u003cextra\\u003e\\u003c\/extra\\u003e\",\"labels\":[\"No\",\"Yes\"],\"marker\":{\"colors\":[\"#209fdf\",\"#092d40\"]},\"showlegend\":true,\"sort\":false,\"textinfo\":\"label+percent\",\"type\":\"pie\",\"values\":[83.9000015258789,16.100000381469727]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"legend\":{},\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"attrition pie chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"}}}<\/script><\/div><figcaption>attrition pie chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-employee-attrition-t1-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-employee-attrition-t1-s1-20260921\" role=\"img\" aria-label=\"attrition Pearson correlations chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: attrition Pearson correlations chart. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"marker\":{\"color\":\"#209fdf\"},\"name\":\"\",\"opacity\":1,\"orientation\":\"h\",\"showlegend\":false,\"type\":\"bar\",\"x\":[-0.22300000488758087,-0.19699999690055847,-0.19499999284744263,-0.1940000057220459,-0.19200000166893005,-0.17800000309944153,-0.17399999499320984,0.1770000010728836,0.24199999868869781,0.2460000067949295],\"y\":[\"total_working_years\",\"years_at_company\",\"age\",\"job_level\",\"monthly_income\",\"years_in_current_role\",\"stock_option_level\",\"marital_status\",\"job_role\",\"over_time\"]}],\"layout\":{\"autosize\":true,\"font\":{\"color\":\"#30343b\",\"family\":\"Arial, sans-serif\",\"size\":14},\"hovermode\":\"closest\",\"margin\":{\"b\":68,\"l\":72,\"pad\":4,\"r\":36,\"t\":112},\"paper_bgcolor\":\"#ffffff\",\"plot_bgcolor\":\"#ffffff\",\"title\":{\"automargin\":true,\"pad\":{\"t\":48},\"text\":\"attrition Pearson correlations chart\",\"x\":0.5,\"xanchor\":\"center\",\"y\":1,\"yanchor\":\"top\",\"yref\":\"container\"},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"range\":[-1,1],\"title\":{\"text\":\"Correlation\"},\"zerolinecolor\":\"#aeb6c2\"},\"yaxis\":{\"automargin\":true,\"autorange\":\"reversed\",\"categoryarray\":[\"total_working_years\",\"years_at_company\",\"age\",\"job_level\",\"monthly_income\",\"years_in_current_role\",\"stock_option_level\",\"marital_status\",\"job_role\",\"over_time\"],\"categoryorder\":\"array\",\"gridcolor\":\"#d9dde3\",\"linecolor\":\"#9aa3ad\",\"type\":\"category\",\"zerolinecolor\":\"#aeb6c2\"}}}<\/script><\/div><figcaption>attrition Pearson correlations chart. Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><div class=\"ndb-note\">This is internal validation using the saved record-level split. Grouped or temporal independence has not been established.<\/div><\/section>\n<span id=\"NeuralNetwork\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"3-model\" class=\"ndb-card\"><h2>3. Model<\/h2><p>The final model has <strong>48 encoded input features<\/strong> and <strong>1 outputs<\/strong>. The following dimensions describe the final saved network.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Layer<\/th><th scope=\"col\">Input shape<\/th><th scope=\"col\">Output shape<\/th><th scope=\"col\">Activation<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Scaling<\/th><td>48<\/td><td>48<\/td><td>\u2014<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>48<\/td><td>3<\/td><td>Tanh<\/td><\/tr><tr><th scope=\"row\">Dense<\/th><td>3<\/td><td>1<\/td><td>Sigmoid<\/td><\/tr><\/tbody><\/table><\/div><p>Output semantics: the sigmoid score increases toward <strong>Yes<\/strong>; No is the other class. Calibration has not been evaluated, so scores are not presented as calibrated probabilities.<\/p><figure class=\"ndb-architecture-figure\" data-model-stage=\"initial\"><a href=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-employee-attrition-t3-s1-20260921.png\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"ndb-architecture\" src=\"https:\/\/www.neuraldesigner.com\/wp-content\/uploads\/2026\/09\/nd-employee-attrition-t3-s1-20260921.png\" alt=\"Exploring patterns in employee attrition: initial Neural Designer architecture\"><\/a><figcaption>Architecture used for this model; no architecture-selection experiment is recorded. Diagram labels show original variables; categorical expansion and the numeric layer dimensions are documented in the model table.<\/figcaption><\/figure><\/section>\n<span id=\"TrainingStrategy\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"4-training\" class=\"ndb-card\"><h2>4. Training strategy<\/h2><p>The saved training configuration uses WeightedSquaredError with QuasiNewton. Training minimizes the recorded objective; the validation subset monitors generalization during fitting. The testing subset is used for the evaluation below.<\/p><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-employee-attrition-t4-s2-20260921\"><div class=\"nd-chart-host\" id=\"nd-employee-attrition-t4-s2-20260921\" role=\"img\" aria-label=\"Quasi-Newton method error history\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Quasi-Newton method error history. Enable JavaScript to explore it.<\/p><script type=\"application\/json\">{\"config\":{\"displayModeBar\":true,\"displaylogo\":false,\"editable\":false,\"responsive\":true,\"scrollZoom\":true,\"toImageButtonOptions\":{\"filename\":\"neural-designer-chart\",\"format\":\"svg\"}},\"data\":[{\"connectgaps\":false,\"line\":{\"color\":\"#529cc2\",\"dash\":\"solid\",\"width\":2},\"mode\":\"lines\",\"name\":\"Training 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Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><h3>Quasi-Newton method results<\/h3><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Measure<\/th><th scope=\"col\">Value<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">Epochs number<\/th><td>120<\/td><\/tr><tr><th scope=\"row\">Elapsed time<\/th><td>00:00:00<\/td><\/tr><tr><th scope=\"row\">Stopping criterion<\/th><td>Maximum validation error increases<\/td><\/tr><tr><th scope=\"row\">Training error<\/th><td>0.353<\/td><\/tr><tr><th scope=\"row\">Validation error<\/th><td>0.467<\/td><\/tr><\/tbody><\/table><\/div><\/section>\n<span id=\"ModelSelection\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"5-selection\" class=\"ndb-card\"><h2>5. Model selection<\/h2><p>No model selection experiment is recorded in this supplied project. The displayed architecture is the trained model used for testing; earlier article claims about a different selected architecture do not apply to this version.<\/p><p>A transparent test-set comparator is the majority-class rule, with accuracy 85.7%. This is a baseline for interpretation, not an alternative model fitted on the test labels.<\/p><\/section>\n<span id=\"TestingAnalysis\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"6-testing\" class=\"ndb-card\"><h2>6. Testing analysis<\/h2><p>The final classifier is evaluated on <strong>294 testing records<\/strong>. The confusion counts below were reproduced from the saved model. Rows are actual classes and columns are predicted classes. The decision threshold is 0.5 on the score for Yes.<\/p><p>Test class prevalence is shown by the support counts. Accuracy is 79.6% and macro F1 is 0.66. ROC AUC is 0.818. The native ROC optimal threshold is descriptive of this test set and is not an independently validated operating policy.<\/p><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Actual \/ predicted<\/th><th scope=\"col\">No<\/th><th scope=\"col\">Yes<\/th><th scope=\"col\">Total<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">No<\/th><td>210<\/td><td>42<\/td><td>252<\/td><\/tr><tr><th scope=\"row\">Yes<\/th><td>18<\/td><td>24<\/td><td>42<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-table-scroll\" tabindex=\"0\"><table><thead><tr><th scope=\"col\">Class<\/th><th scope=\"col\">Test cases<\/th><th scope=\"col\">Sensitivity \/ recall<\/th><th scope=\"col\">Specificity<\/th><th scope=\"col\">Precision \/ PPV<\/th><th scope=\"col\">F1<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">No<\/th><td>252<\/td><td>83.3%<\/td><td>57.1%<\/td><td>92.1%<\/td><td>0.875<\/td><\/tr><tr><th scope=\"row\">Yes<\/th><td>42<\/td><td>57.1%<\/td><td>83.3%<\/td><td>36.4%<\/td><td>0.444<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nd-native-test-figures\"><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-employee-attrition-t5-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-employee-attrition-t5-s1-20260921\" role=\"img\" aria-label=\"ROC chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: ROC chart. 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Exported with Neural Designer from the saved task report.<\/figcaption><\/figure><figure class=\"nd-native-figure\"><div class=\"nd-chart-bundle\" tabindex=\"0\" role=\"group\" aria-label=\"Interactive chart; scroll horizontally on narrow screens\" data-native-chart=\"nd-employee-attrition-t7-s1-20260921\"><div class=\"nd-chart-host\" id=\"nd-employee-attrition-t7-s1-20260921\" role=\"img\" aria-label=\"Cumulative gain chart\"><\/div><p class=\"nd-chart-fallback\">Interactive chart: Cumulative gain chart. 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Exported with Neural Designer from the saved task report. This native curve is expressed in positive\/negative rates. It is a discrimination view, not a validated fraction-of-customers campaign gain.<\/figcaption><\/figure><\/div><\/section>\n<span id=\"ModelDeployment\" class=\"nd-legacy-anchor\" aria-hidden=\"true\"><\/span><section id=\"7-model-deployment\" class=\"ndb-card\"><h2>7. Model deployment<\/h2><p>Validated inputs \u2192 saved preprocessing \u2192 neural network \u2192 score or estimate \u2192 domain review.<\/p><div class=\"nd-model-calculator\" id=\"nd-model-employee-attrition\"><h3>Explore the exported model<\/h3><p>This research demonstration runs locally in your browser. Values outside the training range are outside the validated domain and are rejected. A valid input range does not guarantee that a combination is physically or operationally plausible.<\/p><p>This is a research demonstration, not a validated decision policy.<\/p><div class=\"nd-model-fields\"><\/div><div class=\"nd-model-actions\"><button class=\"aui aui-button nd-model-calculate\" type=\"button\">Calculate<\/button><button class=\"aui aui-button aui-button--secondary nd-model-reset\" type=\"button\">Reset example<\/button><\/div><div class=\"nd-model-result\" role=\"status\" aria-live=\"polite\"><\/div><\/div><script data-noptimize=\"1\">(()=>{function Identity(x) {\n\treturn x;\n}\nfunction Sigmoid(x) {\n\tvar z = 1\/(1+Math.exp(-x));\n\treturn z;\n}\nfunction Tanh(x) {\n\treturn Math.tanh(x);\n}\nfunction calculate_outputs(inputs)\n{\n\tvar age = +inputs[0];\n\tvar business_travel = +inputs[1];\n\tvar daily_rate = +inputs[2];\n\tvar Human_Resources = +inputs[3];\n\tvar Research__amprsn__Development = +inputs[4];\n\tvar Sales = +inputs[5];\n\tvar distance_from_home = +inputs[6];\n\tvar education = +inputs[7];\n\tvar Human_Resources_8 = +inputs[8];\n\tvar Life_Sciences = +inputs[9];\n\tvar Marketing = +inputs[10];\n\tvar Medical = +inputs[11];\n\tvar Other = +inputs[12];\n\tvar Technical_Degree = +inputs[13];\n\tvar employee_number = +inputs[14];\n\tvar environment_satisfaction = +inputs[15];\n\tvar gender = +inputs[16];\n\tvar hourly_rate = +inputs[17];\n\tvar job_involvement = +inputs[18];\n\tvar job_level = +inputs[19];\n\tvar Healthcare_Representative = +inputs[20];\n\tvar Human_Resources_21 = +inputs[21];\n\tvar Laboratory_Technician = +inputs[22];\n\tvar Manager = +inputs[23];\n\tvar Manufacturing_Director = +inputs[24];\n\tvar Research_Director = +inputs[25];\n\tvar Research_Scientist = +inputs[26];\n\tvar Sales_Executive = +inputs[27];\n\tvar Sales_Representative = +inputs[28];\n\tvar job_satisfaction = +inputs[29];\n\tvar Divorced = +inputs[30];\n\tvar Married = +inputs[31];\n\tvar Single = +inputs[32];\n\tvar monthly_income = +inputs[33];\n\tvar monthly_rate = +inputs[34];\n\tvar num_companies_worked = +inputs[35];\n\tvar over_time = +inputs[36];\n\tvar percent_salary_hike = +inputs[37];\n\tvar performance_rating = +inputs[38];\n\tvar relationship_satisfaction = +inputs[39];\n\tvar stock_option_level = +inputs[40];\n\tvar total_working_years = +inputs[41];\n\tvar training_times_last_year = +inputs[42];\n\tvar work_life_balance = +inputs[43];\n\tvar years_at_company = +inputs[44];\n\tvar years_in_current_role = +inputs[45];\n\tvar years_since_last_promotion = +inputs[46];\n\tvar years_with_curr_manager = +inputs[47];\n\n\tvar scaled_age = age*0.1095019132-4.043226719;\n\tvar scaled_business_travel = business_travel*1.879727483-2.042117119;\n\tvar scaled_daily_rate = daily_rate*0.002479101066-1.989443898;\n\tvar scaled_Human_Resources = Human_Resources*2.0-1.0;\n\tvar scaled_Research__amprsn__Development = Research__amprsn__Development*2.0-1.0;\n\tvar scaled_Sales = Sales*2.0-1.0;\n\tvar scaled_distance_from_home = distance_from_home*0.1233943254-1.134304762;\n\tvar scaled_education = education*0.9767436981-2.845185995;\n\tvar scaled_Human_Resources_8 = Human_Resources_8*2.0-1.0;\n\tvar scaled_Life_Sciences = Life_Sciences*2.0-1.0;\n\tvar scaled_Marketing = Marketing*2.0-1.0;\n\tvar scaled_Medical = Medical*2.0-1.0;\n\tvar scaled_Other = Other*2.0-1.0;\n\tvar scaled_Technical_Degree = Technical_Degree*2.0-1.0;\n\tvar scaled_employee_number = employee_number*0.001661626389-1.702951074;\n\tvar scaled_environment_satisfaction = environment_satisfaction*0.9151558876-2.490843773;\n\tvar scaled_gender = gender*2.0-1.0;\n\tvar scaled_hourly_rate = hourly_rate*0.04920654371-3.242278099;\n\tvar scaled_job_involvement = job_involvement*1.405838966-3.837841749;\n\tvar scaled_job_level = job_level*0.9037015438-1.865194917;\n\tvar scaled_Healthcare_Representative = Healthcare_Representative*2.0-1.0;\n\tvar scaled_Human_Resources_21 = Human_Resources_21*2.0-1.0;\n\tvar scaled_Laboratory_Technician = Laboratory_Technician*2.0-1.0;\n\tvar scaled_Manager = Manager*2.0-1.0;\n\tvar scaled_Manufacturing_Director = Manufacturing_Director*2.0-1.0;\n\tvar scaled_Research_Director = Research_Director*2.0-1.0;\n\tvar scaled_Research_Scientist = Research_Scientist*2.0-1.0;\n\tvar scaled_Sales_Executive = Sales_Executive*2.0-1.0;\n\tvar scaled_Sales_Representative = Sales_Representative*2.0-1.0;\n\tvar scaled_job_satisfaction = job_satisfaction*0.9070541263-2.474960566;\n\tvar scaled_Divorced = Divorced*2.0-1.0;\n\tvar scaled_Married = Married*2.0-1.0;\n\tvar scaled_Single = Single*2.0-1.0;\n\tvar scaled_monthly_income = monthly_income*0.0002124788734-1.381735325;\n\tvar scaled_monthly_rate = monthly_rate*0.0001405410294-2.011577845;\n\tvar scaled_num_companies_worked = num_companies_worked*0.4004565179-1.078509569;\n\tvar scaled_over_time = over_time*2.0-1.0;\n\tvar scaled_percent_salary_hike = percent_salary_hike*0.273321867-4.157089233;\n\tvar scaled_performance_rating = performance_rating*2.772387028-8.743387222;\n\tvar scaled_relationship_satisfaction = relationship_satisfaction*0.9252063036-2.509381533;\n\tvar scaled_stock_option_level = stock_option_level*1.174002409-0.9320147038;\n\tvar scaled_total_working_years = total_working_years*0.1285654455-1.450166821;\n\tvar scaled_training_times_last_year = training_times_last_year*0.7758975029-2.171985388;\n\tvar scaled_work_life_balance = work_life_balance*1.415961385-3.909780979;\n\tvar scaled_years_at_company = years_at_company*0.1632802337-1.144294024;\n\tvar scaled_years_in_current_role = years_in_current_role*0.2760974169-1.167685032;\n\tvar scaled_years_since_last_promotion = years_since_last_promotion*0.310430795-0.6791481376;\n\tvar scaled_years_with_curr_manager = years_with_curr_manager*0.2803531289-1.155932307;\n\tvar dense_layer_1_output_0 = Tanh( -0.06297304481 + (-0.2881317735*scaled_age) + (0.3153038919*scaled_business_travel) + (0.1128234863*scaled_daily_rate) + (0.07339716703*scaled_Human_Resources) + (-0.07464993*scaled_Research__amprsn__Development) + (0.02872455493*scaled_Sales) + (0.3588138819*scaled_distance_from_home) + (-0.07807140797*scaled_education) + (0.1738578528*scaled_Human_Resources_8) + (-0.05107174441*scaled_Life_Sciences) + (0.1644274294*scaled_Marketing) + (-0.1915508509*scaled_Medical) + (0.01023522485*scaled_Other) + (0.2694717646*scaled_Technical_Degree) + (0.004386393353*scaled_employee_number) + (-0.2653270364*scaled_environment_satisfaction) + (-0.05166560411*scaled_gender) + (0.08337515593*scaled_hourly_rate) + (-0.04482441396*scaled_job_involvement) + (-0.1523351967*scaled_job_level) + (-0.03377103806*scaled_Healthcare_Representative) + (0.08072490245*scaled_Human_Resources_21) + (0.2013823837*scaled_Laboratory_Technician) + (0.09664583951*scaled_Manager) + (-0.1490647942*scaled_Manufacturing_Director) + (-0.06441482902*scaled_Research_Director) + (0.0584105812*scaled_Research_Scientist) + (-0.1038097218*scaled_Sales_Executive) + (0.4048999548*scaled_Sales_Representative) + (-0.1617586613*scaled_job_satisfaction) + (-0.01767403446*scaled_Divorced) + (-0.1726385951*scaled_Married) + (0.2238017023*scaled_Single) + (-0.1500395536*scaled_monthly_income) + (0.01079560909*scaled_monthly_rate) + (0.09352720529*scaled_num_companies_worked) + (0.4643973708*scaled_over_time) + (0.1132695004*scaled_percent_salary_hike) + (0.01908186451*scaled_performance_rating) + (-0.2354228944*scaled_relationship_satisfaction) + (-0.1691477448*scaled_stock_option_level) + (-0.1828287542*scaled_total_working_years) + (-0.1363200247*scaled_training_times_last_year) + (-0.1909803748*scaled_work_life_balance) + (-0.1477448642*scaled_years_at_company) + (-0.3987634778*scaled_years_in_current_role) + (0.3264983296*scaled_years_since_last_promotion) + (-0.07243721187*scaled_years_with_curr_manager) );\n\tvar dense_layer_1_output_1 = Tanh( -0.02452233806 + (0.03464975208*scaled_age) + (0.2109889686*scaled_business_travel) + (-0.03526102751*scaled_daily_rate) + (-0.0111603234*scaled_Human_Resources) + (0.02047394961*scaled_Research__amprsn__Development) + (0.03831955418*scaled_Sales) + (0.04111713544*scaled_distance_from_home) + (0.05302841216*scaled_education) + (0.04859830812*scaled_Human_Resources_8) + (-0.07959834486*scaled_Life_Sciences) + (-0.02289715596*scaled_Marketing) + (0.1410427392*scaled_Medical) + (-0.1242218316*scaled_Other) + (0.1221361011*scaled_Technical_Degree) + (-0.002801060677*scaled_employee_number) + (-0.09861578792*scaled_environment_satisfaction) + (0.1653536558*scaled_gender) + (-0.1196088716*scaled_hourly_rate) + (-0.04432226345*scaled_job_involvement) + (-0.02072710171*scaled_job_level) + (-0.1466725469*scaled_Healthcare_Representative) + (0.005989941768*scaled_Human_Resources_21) + (0.1123088151*scaled_Laboratory_Technician) + (-0.03383507207*scaled_Manager) + (-0.01418145653*scaled_Manufacturing_Director) + (-0.056630373*scaled_Research_Director) + (0.003614936955*scaled_Research_Scientist) + (0.01365533099*scaled_Sales_Executive) + (0.2340011299*scaled_Sales_Representative) + (-0.1945923716*scaled_job_satisfaction) + (-0.1141336262*scaled_Divorced) + (0.03308037296*scaled_Married) + (0.155637756*scaled_Single) + (-0.07645860314*scaled_monthly_income) + (0.04967748001*scaled_monthly_rate) + (0.185593605*scaled_num_companies_worked) + (0.4290411174*scaled_over_time) + (-0.05053467304*scaled_percent_salary_hike) + (0.06157723069*scaled_performance_rating) + (-0.03577440232*scaled_relationship_satisfaction) + (-0.1527320296*scaled_stock_option_level) + (0.002692412352*scaled_total_working_years) + (-0.02415581234*scaled_training_times_last_year) + (-0.1265264153*scaled_work_life_balance) + (0.05200902*scaled_years_at_company) + (-0.2274563313*scaled_years_in_current_role) + (0.2090747207*scaled_years_since_last_promotion) + (-0.0378520675*scaled_years_with_curr_manager) );\n\tvar dense_layer_1_output_2 = Tanh( 0.05367033184 + (0.1693503559*scaled_age) + (-0.06033506989*scaled_business_travel) + (0.1782106012*scaled_daily_rate) + (0.03898807615*scaled_Human_Resources) + (0.03493056446*scaled_Research__amprsn__Development) + (-0.1571633369*scaled_Sales) + (-0.1380678862*scaled_distance_from_home) + (0.1271897405*scaled_education) + (-0.1309436709*scaled_Human_Resources_8) + (-0.02213144675*scaled_Life_Sciences) + (-0.2101751268*scaled_Marketing) + (-0.1781298816*scaled_Medical) + (0.2526472509*scaled_Other) + (0.06444586068*scaled_Technical_Degree) + (-0.06960269064*scaled_employee_number) + (0.3407594264*scaled_environment_satisfaction) + (-0.2982436121*scaled_gender) + (0.03853823617*scaled_hourly_rate) + (0.2702922821*scaled_job_involvement) + (0.01980320737*scaled_job_level) + (0.1679794788*scaled_Healthcare_Representative) + (0.03248816356*scaled_Human_Resources_21) + (-0.3854677677*scaled_Laboratory_Technician) + (0.07712054253*scaled_Manager) + (0.05199432373*scaled_Manufacturing_Director) + (0.06049687043*scaled_Research_Director) + (-0.006547566503*scaled_Research_Scientist) + (-0.112116918*scaled_Sales_Executive) + (-0.3388078511*scaled_Sales_Representative) + (0.394089371*scaled_job_satisfaction) + (0.1119359881*scaled_Divorced) + (-0.06190078706*scaled_Married) + (-0.1029839367*scaled_Single) + (0.1389195472*scaled_monthly_income) + (-0.1338082552*scaled_monthly_rate) + (-0.1547546387*scaled_num_companies_worked) + (-0.5473276377*scaled_over_time) + (0.2958853245*scaled_percent_salary_hike) + (-0.08448524773*scaled_performance_rating) + (0.2478835285*scaled_relationship_satisfaction) + (0.085304901*scaled_stock_option_level) + (-0.1764101088*scaled_total_working_years) + (0.1149290875*scaled_training_times_last_year) + (0.1878709197*scaled_work_life_balance) + (-0.08395291865*scaled_years_at_company) + (0.2819897234*scaled_years_in_current_role) + (-0.3919793069*scaled_years_since_last_promotion) + (-0.005720890127*scaled_years_with_curr_manager) );\n\tvar attrition = Sigmoid( 0.2913728952 + (1.108665466*dense_layer_1_output_0) + (0.7414293289*dense_layer_1_output_1) + (-1.248243332*dense_layer_1_output_2) );\n\tvar out = [];\n\tout.push(attrition);\n\n\treturn out;\n}\nconst cfg={\"fields\":[{\"name\":\"age\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":18.0,\"max\":60.0,\"value\":24.0,\"unit\":\"\"},{\"name\":\"business_travel\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":2.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"daily_rate\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":102.0,\"max\":1499.0,\"value\":673.0,\"unit\":\"\"},{\"name\":\"department\",\"n\":3,\"type\":\"Categorical\",\"categories\":[\"Human Resources\",\"Research \\u0026 Development\",\"Sales\"],\"min\":0.0,\"max\":1.0,\"value\":1,\"unit\":\"\"},{\"name\":\"distance_from_home\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":29.0,\"value\":11.0,\"unit\":\"\"},{\"name\":\"education\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":5.0,\"value\":2.0,\"unit\":\"\"},{\"name\":\"education_field\",\"n\":6,\"type\":\"Categorical\",\"categories\":[\"Human Resources\",\"Life Sciences\",\"Marketing\",\"Medical\",\"Other\",\"Technical Degree\"],\"min\":0.0,\"max\":1.0,\"value\":4,\"unit\":\"\"},{\"name\":\"employee_number\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":2068.0,\"value\":26.0,\"unit\":\"\"},{\"name\":\"environment_satisfaction\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":4.0,\"value\":1.0,\"unit\":\"\"},{\"name\":\"gender\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"Female\",\"Male\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"hourly_rate\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":30.0,\"max\":100.0,\"value\":96.0,\"unit\":\"\"},{\"name\":\"job_involvement\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":4.0,\"value\":4.0,\"unit\":\"\"},{\"name\":\"job_level\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":5.0,\"value\":2.0,\"unit\":\"\"},{\"name\":\"job_role\",\"n\":9,\"type\":\"Categorical\",\"categories\":[\"Healthcare Representative\",\"Human Resources\",\"Laboratory Technician\",\"Manager\",\"Manufacturing Director\",\"Research Director\",\"Research Scientist\",\"Sales Executive\",\"Sales Representative\"],\"min\":0.0,\"max\":1.0,\"value\":4,\"unit\":\"\"},{\"name\":\"job_satisfaction\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":4.0,\"value\":3.0,\"unit\":\"\"},{\"name\":\"marital_status\",\"n\":3,\"type\":\"Categorical\",\"categories\":[\"Divorced\",\"Married\",\"Single\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"monthly_income\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1009.0,\"max\":19999.0,\"value\":4011.0,\"unit\":\"\"},{\"name\":\"monthly_rate\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":2094.0,\"max\":26999.0,\"value\":8232.0,\"unit\":\"\"},{\"name\":\"num_companies_worked\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":9.0,\"value\":0.0,\"unit\":\"\"},{\"name\":\"over_time\",\"n\":1,\"type\":\"Binary\",\"categories\":[\"No\",\"Yes\"],\"min\":0.0,\"max\":1.0,\"value\":0,\"unit\":\"\"},{\"name\":\"percent_salary_hike\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":11.0,\"max\":25.0,\"value\":18.0,\"unit\":\"\"},{\"name\":\"performance_rating\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":3.0,\"max\":4.0,\"value\":3.0,\"unit\":\"\"},{\"name\":\"relationship_satisfaction\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":4.0,\"value\":4.0,\"unit\":\"\"},{\"name\":\"stock_option_level\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":3.0,\"value\":1.0,\"unit\":\"\"},{\"name\":\"total_working_years\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":40.0,\"value\":5.0,\"unit\":\"\"},{\"name\":\"training_times_last_year\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":6.0,\"value\":5.0,\"unit\":\"\"},{\"name\":\"work_life_balance\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":1.0,\"max\":4.0,\"value\":2.0,\"unit\":\"\"},{\"name\":\"years_at_company\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":40.0,\"value\":4.0,\"unit\":\"\"},{\"name\":\"years_in_current_role\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":18.0,\"value\":2.0,\"unit\":\"\"},{\"name\":\"years_since_last_promotion\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":15.0,\"value\":1.0,\"unit\":\"\"},{\"name\":\"years_with_curr_manager\",\"n\":1,\"type\":\"Numeric\",\"categories\":[],\"min\":0.0,\"max\":17.0,\"value\":3.0,\"unit\":\"\"}],\"output_names\":[\"Yes score\"],\"reference\":[0.06399088641937994]},root=document.getElementById(\"nd-model-employee-attrition\");if(!root)return;const grid=root.querySelector(\".nd-model-fields\"),result=root.querySelector(\".nd-model-result\"),controls=[];cfg.fields.forEach((f,i)=>{const wrap=document.createElement(\"div\");wrap.className=\"nd-model-field\";const label=document.createElement(\"label\"),id=root.id+\"-\"+i;label.htmlFor=id;label.textContent=f.name.replaceAll(\"_\",\" \")+(f.unit?\" (\"+f.unit+\")\":\"\");let input;if(f.categories?.length){input=document.createElement(\"select\");f.categories.forEach((name,k)=>{const option=document.createElement(\"option\");option.value=String(k);option.textContent=name;input.appendChild(option);});}else{input=document.createElement(\"input\");input.type=\"number\";input.step=\"any\";input.min=String(f.min);input.max=String(f.max);}input.id=id;input.value=String(f.value);wrap.append(label,input);grid.appendChild(wrap);controls.push(input);});const calculate=()=>{const inputs=[];let valid=true;cfg.fields.forEach((f,i)=>{const el=controls[i],v=Number(el.value);let bad=el.value.trim()===\"\"||!Number.isFinite(v);if(!f.categories?.length)bad=bad||v<f.min||v>f.max;el.setAttribute(\"aria-invalid\",bad?\"true\":\"false\");if(bad)valid=false;if(f.n>1){for(let k=0;k<f.n;k++)inputs.push(k===v?1:0);}else inputs.push(v);});if(!valid){result.textContent=\"Enter a valid value within every displayed input range.\";return;}let outputs=calculate_outputs(inputs);if(outputs.some(v=>!Number.isFinite(v))){result.textContent=\"The model could not evaluate these inputs.\";return;}result.textContent=outputs.map((v,i)=>cfg.output_names[i]+\": \"+Number(v.toPrecision(7))).join(\"\\n\");root.dataset.outputs=JSON.stringify(outputs);};root.querySelector(\".nd-model-calculate\").addEventListener(\"click\",calculate);root.querySelector(\".nd-model-reset\").addEventListener(\"click\",()=>{controls.forEach((el,i)=>el.value=String(cfg.fields[i].value));calculate();});calculate();})();<\/script><div class=\"ndb-downloads\"><a class=\"aui aui-button aui-button--secondary\" href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Download Neural Designer<\/a><\/div><\/section>\n<section id=\"8-limitations\" class=\"ndb-card\"><h2>8. Evidence and limitations<\/h2><p>Do not use benchmark scores alone for employment decisions. Establish dataset provenance and representativeness, review privacy and sensitive attributes, validate across time and organizational units, and investigate group-level findings with human review. The model is not an estimate of an intervention&#x27;s effect.<\/p><p>No external validation or independent calibration study is included. Preprocessing statistics and model choices should be refitted within a prospective or grouped validation design. Correlations and directional responses describe associations, not causes. Human review is required before an operational decision.<\/p><p>Confidence intervals, subgroup performance, calibration curves and decision-cost validation are not established by these tasks. Predictive values apply to the observed test class distribution and may change when prevalence shifts.<\/p><\/section>\n<section id=\"references\" class=\"ndb-card\"><h2>References<\/h2><ul><li><a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/testing-analysis\/\">Neural Designer testing-analysis documentation<\/a>.<\/li><\/ul><\/section>\n<\/div>\n<\/div>\n<script data-noptimize=\"1\" data-cfasync=\"false\" src=\"https:\/\/cdn.plot.ly\/plotly-basic-4.0.0.min.js\"><\/script><script data-noptimize=\"1\">(()=>{const start=()=>{if(!window.Plotly)return;document.querySelectorAll('.nd-chart-bundle').forEach(bundle=>{if(bundle.dataset.started)return;bundle.dataset.started='1';const host=bundle.querySelector('.nd-chart-host'),source=bundle.querySelector('script[type=\"application\/json\"]');if(!host||!source)return;const figure=JSON.parse(source.textContent);window.Plotly.newPlot(host,figure.data,figure.layout,figure.config).then(async()=>{const title=host.querySelector('.gtitle');if(title)host.style.minWidth=Math.ceil(title.getComputedTextLength()+48)+'px';await window.Plotly.Plots.resize(host);bundle.classList.add('is-ready');if(window.ResizeObserver){let timer;new ResizeObserver(()=>{clearTimeout(timer);timer=setTimeout(()=>window.Plotly.Plots.resize(host),80);}).observe(bundle);}}).catch(error=>{bundle.dataset.error=String(error);console.error(error);});});};const ready=()=>{if(window.Plotly){start();return;}let count=0;const timer=setInterval(()=>{if(window.Plotly){clearInterval(timer);start();}else if(++count>200)clearInterval(timer);},50);};if(document.readyState==='loading')document.addEventListener('DOMContentLoaded',ready,{once:true});else ready();})();<\/script>","protected":false},"author":13,"featured_media":2241,"template":"","categories":[29],"tags":[47],"class_list":["post-3484","learning","type-learning","status-publish","has-post-thumbnail","hentry","category-examples","tag-finance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Prevent employee attrition using machine learning<\/title>\n<meta name=\"description\" content=\"Build a machine learning model to predict your company&#039;s employees&#039; churn, preventing employee attrition, and take measures to avoid it.\" \/>\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\/employee-attrition\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Reducing Employee Attrition machine learning example\" \/>\n<meta property=\"og:description\" content=\"One of the main problems of companies and human resources departments is employee churn. 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