Classification in Depth with Scikit-Learn

# Random Forest Algorithm

During this project, you will learn the fundamentals of random forest and how to implement it using scikit-learn. You will also understand the advantages of using this model over decision trees. Random Forest is an ensemble learning technique that builds multiple decision trees and combines their predictions to generate a final output.
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## Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

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### Prefermance evaluation

Compute the accuracy score, confusion matrix, precision and recall using the testing dataset.

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### Compute precision and recall of the following example

For this task, use the following dataset:

``````# Generate a random dataset for classification
X, y = make_classification(n_samples=1000, n_features=10, random_state=42)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
``````

Your task is to train a Random Forest classifier using different max_depth values and select the correct answers. Here are the instructions:

``````1. Create a list of max_depth values: max_depths = [2, 4, 6, 8, 10,100].
2. Loop through the list of max_depth values and train a RandomForestClassifier for each value. Use also , random_state=42 and  n_estimators=100.
3. Use the trained classifier to make predictions on the test set.
4. Compute the accuracy score for each classifier using the accuracy_score function from scikit-learn.
5. Select the correct answer(s) based on the accuracy score(s), precision and recall.
``````

Good luck!

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### The feature importance score for a feature in a machine learning model indicates how much the feature contributes to the target variable.

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#### Verónica Barraza

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## Classification in Depth with Scikit-Learn

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