Python for Data Science and Machine Learning

Python for Data Science and Machine Learning - Important Points

A. Mean squared error

B. R-squared

C. F1 score

D. None of the above

A. To test the performance of the model on unseen data

B. To create a validation set for hyperparameter tuning

C. To reduce overfitting of the model

D. All of the above

A. Principal Component Analysis (PCA)

B. Recursive Feature Elimination (RFE)

C. K-means clustering

D. Gradient descent

A. Number of features

B. Learning rate

C. Number of samples

D. Target variable

A. To reduce model complexity and prevent overfitting

B. To increase model complexity and prevent underfitting

C. To improve model accuracy on training data

D. None of the above