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Topics To Learn In Decision Tree.
Topics To Learn In Decision Tree. Table Of Contents: Basic Understanding of Decision Trees. Splitting Criteria. Building a Decision Tree. Overfitting and Underfitting. Hyperparameters of Decision Trees. Handling Categorical Data. Evaluation Metrics for Decision Trees. Practical Implementation. Real-world Use Cases. Advancement In Decision Tree. (1) Basic Understanding of Decision Trees. (2) Splitting Criteria (3) Building a Decision Tree (4) Overfitting and Underfitting (5) Hyperparameters of Decision Trees. (6) Handling Categorical Data. (7) Evaluation Metrics for Decision Trees (8) Practical Implementation (9) Real-world Use Cases (10) Advanced Topics
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Interview Questions On Linear Regression.
Interview Questions On Linear Regression. Table Of Contents: What is linear regression? Explain the assumptions of a linear regression model. What is the difference between simple and multiple linear regression? What is multicollinearity, and how do you detect it? What are residuals in linear regression? What is the cost function used in linear regression? How do you find the optimal parameters in linear regression? Explain the formula for the regression line. What is R-squared? What is the adjusted R-squared, and why is it important? How do you handle categorical variables in linear regression? What would you do if your model
