Tuning employee turnover classifier Hrant Davtyan Assistant - - PowerPoint PPT Presentation

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Tuning employee turnover classifier Hrant Davtyan Assistant - - PowerPoint PPT Presentation

Mar 28, 2024 481 likes •652 views

DataCamp Human Resources Analytics: Predicting Employee Churn in Python HUMAN RESOURCES ANALYTICS : PREDICTING EMPLOYEE CHURN IN PYTHON Tuning employee turnover classifier Hrant Davtyan Assistant Professor of Data Science American University

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Tuning employee turnover classifier

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN PYTHON

Hrant Davtyan

Assistant Professor of Data Science American University of Armenia

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Overfitting

Existance of overfitting: Training accuracy: 100% Testing accuracy: 97.23% Methods to fight it: Limiting tree maximum depth Limiting minimum saple size in leafs

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Pruning the tree

Limiting Depth Limiting Samples

model_depth_5 = DecisionTreeClassifier( max_depth=5, random_state=42) # Train set Accuracy: 97.71% # Test set Accuracy: 97.06% model_sample_100 = DecisionTreeClassifier( min_samples_leaf=100, random_state=42) # Train set Accuracy: 96.58% # Test set Accuracy: 96.13%

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Let's practice!

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN PYTHON

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Evaluating the model

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN PYTHON

Hrant Davtyan

Assistant Professor of Data Science American University of Armenia

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Prediction errors

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Evaluation metrics 1

If target is leavers, focus on FN Recall score = TP/(TP+FN) Lower FN, higher Recall score Recall score - % of correct predictions among 1s (leavers) If target is stayers, focus on FP Specificity = TN/(TN+FP) Lower FP, higher Specificity, Specificity - % of correct predictions among 0s (stayers)

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Evaluation metrics 2

Even if target is leavers, you may still focus on FP: Precision score = TP/(TP+FP) Lower FP, higher Recall score Precision score - % of leavers in reality, among those predicted to leave

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Let's practice!

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN PYTHON

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Targeting both leavers and stayers

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN PYTHON

Hrant Davtyan

Assistant Professor of Data Science American University of Armenia

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

AUC score

Vertical axis: Recall Horizontal axis: 1 - Specificity Blue line: ROC Green line: baseline Area between blue and green: AUC

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Let's practice!

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN PYTHON

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Class Imbalance

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN PYTHON

Hrant Davtyan

Assistant Professor of Data Science American University of Armenia

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Prior probabilities

Without balance P = 0.76 P = 0.24 Gini = 0.36 With balance P = 0.5 P = 0.5 Gini = 0.5

1 1

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DataCamp Human Resources Analytics: Predicting Employee Churn in Python

Let's practice!

HUMAN RESOURCES ANALYTICS: PREDICTING EMPLOYEE CHURN IN PYTHON