Hyperparameter tuning (GridSearchCV) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the GridSearchCV class from scikit-learn.

ML Python

from sklearn.model_selection import [1]

Drag options to blanks, or click blank then click option'

AGridSearchCV

Bcross_val_score

Ctrain_test_split

DRandomizedSearchCV

Attempts:

3 left

💡 Hint

Common Mistakes

Importing RandomizedSearchCV instead of GridSearchCV

Importing train_test_split which is for splitting data

Importing cross_val_score which is for scoring models

The GridSearchCV class is imported from sklearn.model_selection to perform hyperparameter tuning using grid search.

2fill in blank

medium

Complete the code to create a parameter grid for tuning the 'max_depth' of a decision tree.

ML Python

param_grid = {'max_depth': [1]

Drag options to blanks, or click blank then click option'

A[1, 2, 3, 4, 5]

B(1, 2, 3, 4, 5)

C{1, 2, 3, 4, 5}

D'1, 2, 3, 4, 5'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a tuple or set instead of a list for parameter values

Using a string instead of a list

Not using a list which causes GridSearchCV to fail

The parameter grid must be a dictionary with keys as parameter names and values as lists of possible values. Here, [1, 2, 3, 4, 5] is a list of depths to try.

3fill in blank

hard

Fix the error in the code to create a GridSearchCV object with a decision tree classifier and the parameter grid.

ML Python

grid_search = GridSearchCV(estimator=[1], param_grid=param_grid, cv=5)

Drag options to blanks, or click blank then click option'

ADecisionTreeClassifier

BDecisionTree()

CDecisionTreeClassifier()

DDecisionTree

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the class name without parentheses

Using a wrong class name like DecisionTree

Not importing DecisionTreeClassifier before use

The estimator argument requires an instance of the model, so you must call DecisionTreeClassifier() with parentheses to create it.

4fill in blank

hard

Fill both blanks to fit the GridSearchCV model on training data and get the best parameters.

ML Python

grid_search.[1](X_train, y_train)
best_params = grid_search.[2]

Drag options to blanks, or click blank then click option'

Afit

Bbest_params_

Cpredict

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train

Trying to call best_params as a method

Using score instead of best_params_ to get parameters

First, you fit the grid search object on training data. Then, you access best_params_ to see the best hyperparameters found.

5fill in blank

hard

Fill all three blanks to print the best score and make predictions on test data using the best estimator.

ML Python

print(f"Best score: {grid_search.[1]:.3f}")
predictions = grid_search.[2].[3](X_test)

Drag options to blanks, or click blank then click option'

Abest_score_

Bbest_estimator_

Cpredict

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using score instead of best_score_

Calling predict directly on grid_search instead of best_estimator_

Confusing best_params_ with best_estimator_

The best_score_ attribute shows the best cross-validation score. The best_estimator_ is the model with the best parameters, and you call its predict method on test data.

Practice

1.

What is the main purpose of using GridSearchCV in machine learning?

easy

A. To split data into training and testing sets

B. To find the best combination of hyperparameters automatically

C. To visualize the model's predictions

D. To reduce the size of the dataset

Solution

1. Step 1: Understand what GridSearchCV does

   GridSearchCV tries different hyperparameter combinations to find the best settings for a model.

2. Step 2: Compare options with GridSearchCV's purpose

   Only To find the best combination of hyperparameters automatically describes this automatic search for best hyperparameters.

3. Final Answer:

   To find the best combination of hyperparameters automatically -> Option B

4. Quick Check:

   GridSearchCV = best hyperparameters [OK]

Hint: GridSearchCV = automatic hyperparameter search [OK]

Common Mistakes:

• Confusing GridSearchCV with data splitting
• Thinking it visualizes predictions
• Assuming it reduces dataset size

2.

Which of the following is the correct way to import GridSearchCV from sklearn?

easy

A. import GridSearchCV from sklearn

B. from sklearn.grid_search import GridSearchCV

C. from sklearn.model_selection import GridSearchCV

D. from sklearn.tuning import GridSearchCV

Solution

1. Step 1: Recall the correct sklearn module for GridSearchCV

   GridSearchCV is in the model_selection module of sklearn.

2. Step 2: Match the correct import syntax

   from sklearn.model_selection import GridSearchCV uses the correct module and syntax for import.

3. Final Answer:

   from sklearn.model_selection import GridSearchCV -> Option C

4. Quick Check:

   Correct import = from sklearn.model_selection import GridSearchCV [OK]

Hint: GridSearchCV is in sklearn.model_selection [OK]

Common Mistakes:

• Using deprecated sklearn.grid_search module
• Wrong import syntax like 'import GridSearchCV from sklearn'
• Assuming a nonexistent sklearn.tuning module

3.

What will be the output of the following code snippet?

from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV

iris = load_iris()
X, y = iris.data, iris.target

param_grid = {'kernel': ['linear', 'rbf'], 'C': [1, 10]}
svc = SVC()
grid = GridSearchCV(svc, param_grid, cv=3)
grid.fit(X, y)
print(grid.best_params_)

medium

A. A dictionary with best kernel and C values found by cross-validation

B. {'kernel': 'rbf', 'C': 10}

C. {'kernel': 'linear', 'C': 1}

D. An error because 'cv' parameter is missing

Solution

1. Step 1: Understand GridSearchCV output

   GridSearchCV's best_params_ attribute returns the best hyperparameters found after testing all combinations.

2. Step 2: Analyze the code snippet

   The code runs GridSearchCV with kernel and C values, then prints the best combination as a dictionary.

3. Final Answer:

   A dictionary with best kernel and C values found by cross-validation -> Option A

4. Quick Check:

   grid.best_params_ = best hyperparameters dict [OK]

Hint: grid.best_params_ shows best hyperparameters found [OK]

Common Mistakes:

• Expecting fixed values instead of dynamic best params
• Thinking cv parameter is missing (it is present)
• Confusing output with training scores

4.

Identify the error in this GridSearchCV usage:

from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV

param_grid = {'C': [0.1, 1, 10]}
svc = SVC()
grid = GridSearchCV(svc, param_grid)
grid.fit(X_train, y_train)
print(grid.best_score_)

Assuming X_train and y_train are defined.

medium

A. Missing cross-validation parameter cv in GridSearchCV

B. Parameter grid keys should be strings with double underscores

C. SVC requires kernel parameter in param_grid

D. No error; code runs correctly and prints best score

Solution

1. Step 1: Check default behavior of GridSearchCV

   If cv is not given, GridSearchCV uses default 5-fold cross-validation.

2. Step 2: Verify param_grid and SVC usage

   Parameter grid keys like 'C' are correct; kernel is optional with default 'rbf'.

3. Final Answer:

   No error; code runs correctly and prints best score -> Option D

4. Quick Check:

   Default cv=5, param_grid keys correct [OK]

Hint: GridSearchCV uses 5-fold CV by default if cv missing [OK]

Common Mistakes:

• Assuming cv parameter is mandatory
• Thinking param_grid keys need double underscores
• Believing kernel must be in param_grid

5.

You want to tune a RandomForestClassifier using GridSearchCV. You want to try n_estimators values 50, 100 and max_depth values 5, 10. Which code snippet correctly sets up the grid search?

hard

A. param_grid = {'n_estimators': [50, 100], 'max_depth': [5, 10]}
rf = RandomForestClassifier()
grid = GridSearchCV(rf, param_grid, cv=4)

B. param_grid = {'RandomForestClassifier__n_estimators': [50, 100], 'RandomForestClassifier__max_depth': [5, 10]}
rf = RandomForestClassifier()
grid = GridSearchCV(rf, param_grid)

C. param_grid = {'n_estimators': 50 or 100, 'max_depth': 5 or 10}
rf = RandomForestClassifier()
grid = GridSearchCV(rf, param_grid)

D. param_grid = {'n_estimators': [50, 100], 'max_depth': [5, 10]}
rf = RandomForestClassifier()
grid = GridSearchCV(rf, param_grid, cv='invalid')

Solution

1. Step 1: Define param_grid correctly for RandomForestClassifier

   Use keys as hyperparameter names with list of values: {'n_estimators': [50, 100], 'max_depth': [5, 10]}.

2. Step 2: Setup GridSearchCV with valid cv parameter

   The correct snippet initializes GridSearchCV with the proper param_grid using lists and a valid integer cv value (e.g., cv=4). Distractors include invalid cv values like strings, incorrect parameter key prefixes, and non-list value syntax like 'or'.

3. Final Answer:

   param_grid = {'n_estimators': [50, 100], 'max_depth': [5, 10]} rf = RandomForestClassifier() grid = GridSearchCV(rf, param_grid, cv=4) -> Option A

4. Quick Check:

   Correct param_grid and valid cv = param_grid = {'n_estimators': [50, 100], 'max_depth': [5, 10]} rf = RandomForestClassifier() grid = GridSearchCV(rf, param_grid, cv=4) [OK]

Hint: Use param names as keys and valid cv number [OK]

Common Mistakes:

• Adding model name prefix to param_grid keys
• Using 'or' instead of list for values
• Setting invalid cv values like strings