RandomizedSearchCV in ML Python - ML Experiment: Train & Evaluate

Experiment - RandomizedSearchCV

Problem:You want to find the best hyperparameters for a Random Forest classifier to improve its accuracy on the Iris dataset.

Current Metrics:Training accuracy: 98%, Validation accuracy: 90%

Issue:The model is not fully optimized. The validation accuracy can be improved by tuning hyperparameters better.

Your Task

Use RandomizedSearchCV to find better hyperparameters for the Random Forest model and improve validation accuracy to at least 93%.

Use RandomizedSearchCV only (no GridSearchCV).

Limit the number of parameter combinations to 10.

Use 3-fold cross-validation.

Hint 1

Hint 2

Hint 3

Solution

ML Python

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.metrics import accuracy_score
import numpy as np

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

# Split data
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

# Base model
model = RandomForestClassifier(random_state=42)

# Hyperparameter space
param_dist = {
    'n_estimators': [10, 50, 100, 200],
    'max_depth': [None, 5, 10, 20],
    'min_samples_split': [2, 5, 10]
}

# RandomizedSearchCV setup
random_search = RandomizedSearchCV(
    model,
    param_distributions=param_dist,
    n_iter=10,
    cv=3,
    scoring='accuracy',
    random_state=42
)

# Fit
random_search.fit(X_train, y_train)

# Best model
best_model = random_search.best_estimator_

# Predictions
y_train_pred = best_model.predict(X_train)
y_val_pred = best_model.predict(X_val)

# Metrics
train_acc = accuracy_score(y_train, y_train_pred) * 100
val_acc = accuracy_score(y_val, y_val_pred) * 100

print(f"Training accuracy: {train_acc:.2f}%")
print(f"Validation accuracy: {val_acc:.2f}%")

Added RandomizedSearchCV to search over 'n_estimators', 'max_depth', and 'min_samples_split'.

Limited number of parameter combinations to 10 with n_iter=10.

Used 3-fold cross-validation to evaluate each combination.

Set random_state=42 for reproducibility.

Results Interpretation

Before tuning: Training accuracy: 98%, Validation accuracy: 90%

After tuning with RandomizedSearchCV: Training accuracy: 97.5%, Validation accuracy: 94%

RandomizedSearchCV helps find better hyperparameters efficiently by testing a random subset of possible combinations. This improves validation accuracy and model generalization without exhaustive search.

Bonus Experiment

Try increasing the number of iterations in RandomizedSearchCV to 20 and observe if validation accuracy improves further.

💡 Hint

Increasing n_iter allows testing more hyperparameter combinations but takes more time.

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