All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
Store the variables in X_test, X_train, y_train and y_test.
For this task implement the log normalization to the Monetary (c.c. blood), and store the new variable called monetary_log and the other features in X_train_normed and X_test_normed.
In the previous page, we performed a variable transformation on the numerical features (logarithmic transformation) to improve their distribution.
Based on this scenario, select the correct statement:
Evaluate the model's performance on the training and testing sets. Based on this performance, select the correct statement.
Evaluate the model's fit based on its performance and select the correct term that corresponds to the given scenario.
To assess model performance and find the optimal hyperparameter value, we can plot a validation curve. Based on this concept, select the correct statement:
Based on the following figure, identify the best max_depth hyperparameter to train a decision tree.
Store the precision, recall, and f1-score of the positive class in the variables precision, recall, and f_1_score
