RandomizedSearchCV in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

ML Python

from sklearn.model_selection import [1]

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

ARandomizedSearchCV

Btrain_test_split

CGridSearchCV

Dcross_val_score

Attempts:

3 left

💡 Hint

Common Mistakes

Importing GridSearchCV instead of RandomizedSearchCV

Importing from the wrong module like sklearn.metrics

The RandomizedSearchCV class is imported from sklearn.model_selection to perform randomized hyperparameter search.

2fill in blank

medium

Complete the code to create a RandomizedSearchCV object with a model and parameter distribution.

ML Python

search = RandomizedSearchCV(estimator=[1], param_distributions=param_dist, n_iter=10, cv=5)

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

Ay_train

Bparam_dist

CX_train

Dmodel

Attempts:

3 left

💡 Hint

Common Mistakes

Passing parameter distribution instead of model as estimator

Passing training data instead of model

The estimator argument expects the model object to tune, such as a classifier or regressor.

3fill in blank

hard

Fix the error in the code to fit the RandomizedSearchCV object on training data.

ML Python

search.[1](X_train, y_train)

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Afit

Bpredict

Cscore

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit

Using score or transform which are not for training

The fit method trains the RandomizedSearchCV object on the training data to find the best parameters.

4fill in blank

hard

Fill both blanks to access the best parameters and best score after fitting.

ML Python

best_params = search.[1]
best_score = search.[2]

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Abest_params_

Bbest_score_

Cbest_estimator_

Dcv_results_

Attempts:

3 left

💡 Hint

Common Mistakes

Using best_estimator_ instead of best_params_ for parameters

Trying to call these as methods instead of attributes

After fitting, best_params_ gives the best found parameters, and best_score_ gives the best cross-validation score.

5fill in blank

hard

Fill all three blanks to create a parameter distribution dictionary for RandomizedSearchCV.

ML Python

param_dist = {
    'n_estimators': [1],
    'max_depth': [2],
    'criterion': [3]
}

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

Arange(10, 101, 10)

B[None, 10, 20, 30]

C['gini', 'entropy']

D[1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Using single values instead of lists or ranges

Mixing data types incorrectly

The parameter distribution dictionary uses ranges or lists of possible values for each hyperparameter to explore during randomized search.

Practice

1. What is the main purpose of using RandomizedSearchCV in machine learning?

easy

A. To find the best model parameters by trying random combinations

B. To train the model on the entire dataset without validation

C. To reduce the size of the dataset by random sampling

D. To visualize the model's predictions on random data points

Solution

1. Step 1: Understand the role of RandomizedSearchCV

   RandomizedSearchCV tries random combinations of model parameters to find the best settings efficiently.

2. Step 2: Compare with other options

   Options B, C, and D describe unrelated tasks like training without validation, data sampling, or visualization, which are not the purpose of RandomizedSearchCV.

3. Final Answer:

   To find the best model parameters by trying random combinations -> Option A

4. Quick Check:

   RandomizedSearchCV = random parameter search [OK]

Hint: RandomizedSearchCV tries random settings to find best model [OK]

Common Mistakes:

• Confusing RandomizedSearchCV with data sampling methods
• Thinking it trains without validation
• Assuming it visualizes data

2. Which of the following is the correct way to import RandomizedSearchCV from scikit-learn?

easy

A. import RandomizedSearchCV from sklearn.model_selection

B. from sklearn.random import RandomizedSearchCV

C. from sklearn.model_selection import RandomizedSearchCV

D. from sklearn.search import RandomizedSearchCV

Solution

1. Step 1: Recall the correct import path

   RandomizedSearchCV is part of the model_selection module in scikit-learn, so the correct import is from sklearn.model_selection.

2. Step 2: Check syntax correctness

   from sklearn.model_selection import RandomizedSearchCV uses the correct Python import syntax. Options B and D use wrong module names, and import RandomizedSearchCV from sklearn.model_selection uses incorrect import syntax.

3. Final Answer:

   from sklearn.model_selection import RandomizedSearchCV -> Option C

4. Quick Check:

   Correct import = sklearn.model_selection [OK]

Hint: Remember RandomizedSearchCV is in sklearn.model_selection [OK]

Common Mistakes:

• Using wrong module names like sklearn.random
• Incorrect import syntax like 'import X from Y'
• Confusing with other sklearn modules

3. Given the code below, what will print(random_search.best_params_) output?

from sklearn.datasets import load_iris
from sklearn.svm import SVC
from sklearn.model_selection import RandomizedSearchCV
import numpy as np

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

param_dist = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}
model = SVC()
random_search = RandomizedSearchCV(model, param_distributions=param_dist, n_iter=2, random_state=42)
random_search.fit(X, y)
print(random_search.best_params_)

medium

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

B. Raises an error because n_iter is too small

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

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

Solution

1. Step 1: Understand RandomizedSearchCV behavior with n_iter=2

   RandomizedSearchCV tries 2 random parameter combinations from the given param_dist. With random_state=42, the selected combos are reproducible.

2. Step 2: Check which parameters are chosen

   Running this code selects {'C': 10, 'kernel': 'rbf'} as best params based on scoring on iris data.

3. Final Answer:

   {'C': 10, 'kernel': 'rbf'} -> Option A

4. Quick Check:

   Random seed + n_iter=2 picks {'C':10,'kernel':'rbf'} [OK]

Hint: RandomizedSearchCV picks random combos; seed fixes output [OK]

Common Mistakes:

• Assuming all parameter combos are tried
• Thinking n_iter too small causes error
• Ignoring random_state effect on results

4. You wrote this code but get an error: TypeError: RandomizedSearchCV.__init__() got an unexpected keyword argument 'param_grid'. What is the mistake?

from sklearn.model_selection import RandomizedSearchCV
from sklearn.tree import DecisionTreeClassifier
param_grid = {'max_depth': [3, 5, 7]}
random_search = RandomizedSearchCV(estimator=DecisionTreeClassifier(), param_grid=param_grid, n_iter=3)
random_search.fit(X_train, y_train)

medium

A. n_iter must be larger than the number of parameters

B. You must import DecisionTreeClassifier before using it

C. RandomizedSearchCV does not accept n_iter parameter

D. Use 'param_distributions' instead of 'param_grid' for RandomizedSearchCV

Solution

1. Step 1: Identify the error cause

   The error says 'param_grid' is unexpected. RandomizedSearchCV expects 'param_distributions' instead of 'param_grid'.

2. Step 2: Correct the parameter name

   Replacing 'param_grid' with 'param_distributions' fixes the error and allows random search to work.

3. Final Answer:

   Use 'param_distributions' instead of 'param_grid' for RandomizedSearchCV -> Option D

4. Quick Check:

   RandomizedSearchCV needs param_distributions [OK]

Hint: RandomizedSearchCV uses param_distributions, not param_grid [OK]

Common Mistakes:

• Confusing RandomizedSearchCV with GridSearchCV parameter names
• Ignoring error message details
• Assuming n_iter is invalid parameter

5. You want to tune a Random Forest model with many parameters but have limited time. Which approach best uses RandomizedSearchCV to find good parameters efficiently?

hard

A. Set n_iter equal to all possible parameter combinations for exhaustive search

B. Set a reasonable n_iter to try a random subset of parameter combos and use cross-validation

C. Use RandomizedSearchCV without specifying param_distributions to try defaults

D. Use RandomizedSearchCV with n_iter=1 to save time

Solution

1. Step 1: Understand the goal of RandomizedSearchCV

   It helps find good parameters by trying a random sample of combinations, which is faster than exhaustive search.

2. Step 2: Choose the best strategy for limited time

   Setting n_iter to a reasonable number balances search quality and time. Using cross-validation ensures reliable results.

3. Final Answer:

   Set a reasonable n_iter to try a random subset of parameter combos and use cross-validation -> Option B

4. Quick Check:

   RandomizedSearchCV = random subset + cross-validation [OK]

Hint: Balance n_iter and validation for efficient tuning [OK]

Common Mistakes:

• Trying all combos defeats RandomizedSearchCV purpose
• Not specifying param_distributions causes errors
• Using n_iter=1 usually gives poor results