Hyperparameter tuning (GridSearchCV) in ML Python

Introduction

We use hyperparameter tuning to find the best settings for a machine learning model so it works well on new data.

When you want to improve your model's accuracy by trying different settings.

When you have a model with options like tree depth or number of neighbors to choose from.

When you want to avoid guessing which settings work best.

When you want to compare many combinations of settings automatically.

Syntax

ML Python

from sklearn.model_selection import GridSearchCV

model = SomeModel()
param_grid = {'param1': [values], 'param2': [values]}
grid = GridSearchCV(model, param_grid, cv=number_of_folds)
grid.fit(X_train, y_train)

best_model = grid.best_estimator_
best_params = grid.best_params_

GridSearchCV tries all combinations of parameters you give it.

cv means how many parts to split your data for testing during tuning.

Examples

This tries decision trees with different depths and split sizes using 3-fold cross-validation.

ML Python

from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()
param_grid = {'max_depth': [3, 5, 7], 'min_samples_split': [2, 4]}
grid = GridSearchCV(model, param_grid, cv=3)
grid.fit(X_train, y_train)

This tries different SVM settings with 5-fold cross-validation.

ML Python

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

model = SVC()
param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}
grid = GridSearchCV(model, param_grid, cv=5)
grid.fit(X_train, y_train)

Sample Model

This program loads the iris flower data, splits it, and uses GridSearchCV to find the best decision tree settings. Then it tests the best model and prints accuracy.

ML Python

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load data
X, y = load_iris(return_X_y=True)

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Define model and parameters
dt = DecisionTreeClassifier(random_state=42)
param_grid = {'max_depth': [2, 3, 4], 'min_samples_split': [2, 3]}

# Setup GridSearchCV
grid_search = GridSearchCV(dt, param_grid, cv=3)

# Train with grid search
grid_search.fit(X_train, y_train)

# Best model and parameters
best_model = grid_search.best_estimator_
best_params = grid_search.best_params_

# Predict and evaluate
predictions = best_model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)

print(f"Best Parameters: {best_params}")
print(f"Test Accuracy: {accuracy:.3f}")

Important Notes

GridSearchCV can take time if you try many parameters or large data.

Use random_state to get the same results every time.

You can check grid_search.cv_results_ to see scores for all tries.

Summary

GridSearchCV helps find the best model settings automatically.

It tries all combinations you give and tests them with cross-validation.

Use it to improve your model's accuracy without guessing.

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