RandomizedSearchCV in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding RandomizedSearchCV Sampling

Which statement best describes how RandomizedSearchCV selects hyperparameter combinations during tuning?

A It tries every possible combination of hyperparameters exhaustively.

B It randomly samples a fixed number of hyperparameter combinations from the specified distributions.

C It selects hyperparameters based on a gradient descent optimization.

D It uses a genetic algorithm to evolve hyperparameter sets over iterations.

❓ Predict Output

intermediate

Output of RandomizedSearchCV Best Parameters

What will be the output of the following code snippet?

ML Python

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
import numpy as np

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

param_dist = {'n_estimators': [10, 50], 'max_depth': [2, 4]}
clf = RandomForestClassifier(random_state=42)

search = RandomizedSearchCV(clf, param_distributions=param_dist, n_iter=2, random_state=42)
search.fit(X, y)
print(search.best_params_)

A {'n_estimators': 10, 'max_depth': 2}

B {'n_estimators': 50, 'max_depth': 4}

C {'n_estimators': 10, 'max_depth': 4}

D {'n_estimators': 50, 'max_depth': 2}

❓ Hyperparameter

advanced

Choosing n_iter in RandomizedSearchCV

What is the effect of increasing the n_iter parameter in RandomizedSearchCV?

A It increases the number of hyperparameter combinations tested, potentially improving model performance but increasing computation time.

B It sets the maximum depth of the decision trees in the model.

C It decreases the number of cross-validation folds used during evaluation.

D It controls the learning rate of the model during training.

❓ Metrics

advanced

Evaluating RandomizedSearchCV Results

After running RandomizedSearchCV, which attribute gives the mean test score for each parameter combination tested?

A search.best_score_

B search.score(X_test, y_test)

C search.cv_results_['mean_test_score']

D search.best_params_

🔧 Debug

expert

Debugging RandomizedSearchCV with Incorrect Parameter Distribution

What error will the following code raise?

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV

iris = load_iris()
X_train, y_train = iris.data, iris.target

param_dist = {'n_estimators': 100, 'max_depth': [3, 5, 7]}
clf = RandomForestClassifier()
search = RandomizedSearchCV(clf, param_distributions=param_dist, n_iter=2)
search.fit(X_train, y_train)

A ValueError: Parameter distribution for 'n_estimators' must be a list or distribution

B No error, runs successfully

C AttributeError: 'RandomForestClassifier' object has no attribute 'fit'

D TypeError: 'int' object is not iterable