Predicting Employee Attrition using Decision Trees

Using employee data to categorise the probability that an employee would depart from a company is known as decision tree-based employee attrition prediction. Because decision trees can handle both category and numerical data and are easy to understand and analyse, they are a popular choice for this task. HR teams can find trends that may point to a higher risk of turnover by using decision trees to analyse historical employee data, including demographics, job satisfaction, salary, tenure, and performance.

Important Procedures for Using Decision Trees to Forecast Employee Attrition
Information Gathering: Compile pertinent employee information such as age, job role, pay, work environment, tenure, promotions, and performance reviews that may have an impact on turnover.

• Data Preprocessing: Clean and preprocess the data to handle missing values, convert categorical data (like department or job role) into numerical representations, and balance the dataset if necessary to handle class imbalance (since attrition events might be fewer than retention cases).
• Building the Decision Tree: Train a decision tree classifier on the preprocessed data. The tree splits the data into branches based on decision points (features) that best separate employees who have left from those who have stayed. The decision tree chooses splits based on criteria like Gini impurity or entropy, aiming to reduce uncertainty in each branch.
• Evaluation: Evaluate the model using metrics like accuracy, precision, recall, and F1 score to assess its performance and ability to correctly classify employees at risk of leaving. Cross-validation is often applied to ensure generalizability.
• Interpretation and Insights: Decision trees are highly interpretable, allowing HR teams to see which factors (like low job satisfaction or lack of promotion) are most indicative of potential attrition. These insights can guide retention strategies, such as improving work conditions or offering growth opportunities.