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Challenge: Comparing Models

Now you'll compare the models we've covered using a single dataset — the breast cancer dataset. The target variable is the 'diagnosis' column, where 1 represents malignant and 0 represents benign cases.

You will apply GridSearchCV to each model to find the best parameters. In this task, you'll use recall as the scoring metric because minimizing false negatives is crucial. To have GridSearchCV select the best parameters based on recall, set scoring='recall'.

Task

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You are given a breast cancer dataset stored as a DataFrame in the df variable.

Create a dictionary for GridSearchCV to iterate through [3, 5, 7, 12] values for n_neighbors and store it in the knn_params variable.
Create a dictionary for GridSearchCV to iterate through [0.1, 1, 10] values for C and store it in the lr_params variable.
Create a dictionary for GridSearchCV to iterate through [2, 4, 6, 10] values for max_depth and [1, 2, 4, 7] values for min_samples_leaf, and store it in the dt_params variable.
Create a dictionary for GridSearchCV to iterate through [2, 4, 6] values for max_depth and [20, 50, 100] values for n_estimators, and store it in the rf_params variable.
Initialize and train a GridSearchCV object for each of the model, and store the trained models in the respective variables: knn_grid, lr_grid, dt_grid, and rf_grid.

Solution

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Section 5. Chapter 3

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Challenge: Comparing Models

Task

Swipe to start coding

You are given a breast cancer dataset stored as a DataFrame in the df variable.

Create a dictionary for GridSearchCV to iterate through [3, 5, 7, 12] values for n_neighbors and store it in the knn_params variable.
Create a dictionary for GridSearchCV to iterate through [0.1, 1, 10] values for C and store it in the lr_params variable.
Create a dictionary for GridSearchCV to iterate through [2, 4, 6, 10] values for max_depth and [1, 2, 4, 7] values for min_samples_leaf, and store it in the dt_params variable.
Create a dictionary for GridSearchCV to iterate through [2, 4, 6] values for max_depth and [20, 50, 100] values for n_estimators, and store it in the rf_params variable.
Initialize and train a GridSearchCV object for each of the model, and store the trained models in the respective variables: knn_grid, lr_grid, dt_grid, and rf_grid.

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 5. Chapter 3

Switch to desktop for real-world practiceContinue from where you are using one of the options below