# Challenge: Comparing Models

Now we will compare the models we learned on one dataset. This is a breast cancer dataset. The target is the `'diagnosis'`

column (1 – malignant, 0 – benign).

We will apply `GridSearchCV`

to each model to find the best parameters. Also, in this task, we would use the **recall** metric for scoring since we do not want to have False Negatives. `GridSearchCV`

can choose the parameters based on the **recall** metric if you set `scoring='recall'`

.

Task

The task is to build all the models we learned and to print the best parameters along with the best recall score of each model. You will need to fill in the parameter names in the `param_grid`

dictionaries.

- For the k-NN model find the best
`n_neighbors`

value out of`[3, 5, 7, 12]`

. - For the Logistic Regression run through
`[0.1, 1, 10]`

values of`C`

. - For a Decision Tree, we want to configure two parameters,
`max_depth`

and`min_samples_leaf`

. Run through values`[2, 4, 6, 10]`

for`max_depth`

and`[1, 2, 4, 7]`

for`min_samples_leaf`

. - For a Random Forest, find the best
`max_depth`

(maximum depth of each Tree) value out of`[2, 4, 6]`

and the best number of trees(`n_estimators`

). Try values`[20, 50, 100]`

for the number of trees.

Note

The code takes some time to run(less than a minute).

Everything was clear?

Course Content

Classification with Python

## Classification with Python

5. Comparing Models

# Challenge: Comparing Models

Now we will compare the models we learned on one dataset. This is a breast cancer dataset. The target is the `'diagnosis'`

column (1 – malignant, 0 – benign).

We will apply `GridSearchCV`

to each model to find the best parameters. Also, in this task, we would use the **recall** metric for scoring since we do not want to have False Negatives. `GridSearchCV`

can choose the parameters based on the **recall** metric if you set `scoring='recall'`

.

Task

The task is to build all the models we learned and to print the best parameters along with the best recall score of each model. You will need to fill in the parameter names in the `param_grid`

dictionaries.

- For the k-NN model find the best
`n_neighbors`

value out of`[3, 5, 7, 12]`

. - For the Logistic Regression run through
`[0.1, 1, 10]`

values of`C`

. - For a Decision Tree, we want to configure two parameters,
`max_depth`

and`min_samples_leaf`

. Run through values`[2, 4, 6, 10]`

for`max_depth`

and`[1, 2, 4, 7]`

for`min_samples_leaf`

. - For a Random Forest, find the best
`max_depth`

(maximum depth of each Tree) value out of`[2, 4, 6]`

and the best number of trees(`n_estimators`

). Try values`[20, 50, 100]`

for the number of trees.

Note

The code takes some time to run(less than a minute).

Everything was clear?