Challenge - 5 Problems

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GridSearchCV Master

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🧠 Conceptual

intermediate

1:30remaining

Understanding GridSearchCV's purpose

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

ATo visualize the training data in multiple dimensions.

BTo reduce the size of the training dataset automatically.

CTo find the best combination of hyperparameters by exhaustively searching a specified parameter grid using cross-validation.

DTo train a model faster by skipping validation steps.

Attempts:

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❓ Predict Output

intermediate

2:00remaining

Output of GridSearchCV best parameters

What will be the output of print(grid_search.best_params_) after running the following code?

ML Python

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 = {'C': [0.1, 1], 'kernel': ['linear', 'rbf']}
svc = SVC()
grid_search = GridSearchCV(svc, param_grid, cv=3)
grid_search.fit(X, y)
print(grid_search.best_params_)

A{'C': 1, 'kernel': 'rbf'}

B{'C': 0.1, 'kernel': 'rbf'}

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

D{'C': 0.1, 'kernel': 'linear'}

Attempts:

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❓ Hyperparameter

advanced

2:00remaining

Choosing hyperparameters for GridSearchCV

Which set of hyperparameters is most appropriate to tune for a Random Forest classifier using GridSearchCV to improve model performance?

A{'learning_rate': [0.01, 0.1], 'activation': ['relu', 'tanh']}

B{'n_estimators': [10, 50, 100], 'max_depth': [None, 10, 20], 'min_samples_split': [2, 5]}

C{'kernel': ['linear', 'poly'], 'C': [0.1, 1]}

D{'alpha': [0.0001, 0.001], 'max_iter': [1000, 2000]}

Attempts:

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❓ Metrics

advanced

1:30remaining

Evaluating GridSearchCV results

After running GridSearchCV, which attribute provides the mean test scores for each parameter combination tested?

Agrid_search.best_score_

Bgrid_search.score(X_test, y_test)

Cgrid_search.best_params_

Dgrid_search.cv_results_['mean_test_score']

Attempts:

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🔧 Debug

expert

2:00remaining

Identifying error in GridSearchCV usage

What error will the following code raise when executed?

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

ANameError because 'grid_search' is not defined

BTypeError because 'param_grid' has invalid types

CValueError because 'cv' parameter is invalid

DNo error, code runs successfully

Attempts:

2 left