Tutorial at SciPy 2018 on Machine Learning with Scikit-Learn, Part 1 by Lemaitre and Grisel

Machine learning is a rapidly growing field that involves the development of algorithms and models that can learn from and make predictions or decisions based on data. One popular library for implementing machine learning algorithms in Python is Scikit-Learn. In this tutorial, we will cover the basics of machine learning with Scikit-Learn as presented by Lemaitre and Grisel in their SciPy 2018 tutorial.

Scikit-Learn is a powerful and user-friendly library for machine learning in Python. It provides a wide variety of machine learning algorithms, such as classification, regression, clustering, and dimensionality reduction. In this tutorial, we will cover the basics of machine learning with Scikit-Learn, including loading data, preprocessing data, building and evaluating models, and optimizing hyperparameters.

To get started with this tutorial, you will need to have Python and Scikit-Learn installed on your machine. You can install Scikit-Learn using pip by running the following command:

pip install scikit-learn

Once you have Scikit-Learn installed, you can now start working with machine learning algorithms. Let’s start by loading a dataset. Scikit-Learn provides a number of built-in datasets that you can use for learning and experimentation. For this tutorial, we will use the famous Iris dataset, which contains measurements of iris flowers.

from sklearn import datasets

# Load the Iris dataset
iris = datasets.load_iris()

# Display the data
print(iris.data)
print(iris.target)

The Iris dataset consists of four features (sepal length, sepal width, petal length, and petal width) and three classes of iris flowers (Setosa, Versicolor, and Virginica). The iris.data array contains the feature values, while the iris.target array contains the class labels.

Next, we will split the dataset into training and testing sets. This is important to evaluate the performance of our machine learning model.

from sklearn.model_selection import train_test_split

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

Now that we have our training and testing sets, we can build a machine learning model. For this tutorial, we will use a simple decision tree classifier.

from sklearn.tree import DecisionTreeClassifier

# Create a decision tree classifier
clf = DecisionTreeClassifier()

# Train the classifier on the training data
clf.fit(X_train, y_train)

# Make predictions on the test data
y_pred = clf.predict(X_test)

# Evaluate the model
accuracy = clf.score(X_test, y_test)
print("Accuracy:", accuracy)

In this code snippet, we created a decision tree classifier using the DecisionTreeClassifier class and trained it on the training data using the fit method. We then made predictions on the test data using the predict method and computed the accuracy of the model using the score method.

Finally, we can optimize the hyperparameters of our model using grid search cross-validation.

from sklearn.model_selection import GridSearchCV

# Define the hyperparameters to search
param_grid = {
    'max_depth': [2, 4, 6, 8, 10],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4]
}

# Create a grid search with cross-validation
grid_search = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5)

# Fit the grid search to the training data
grid_search.fit(X_train, y_train)

# Get the best parameters
best_params = grid_search.best_params_

# Re-train the model with the best parameters
clf_best = DecisionTreeClassifier(**best_params)
clf_best.fit(X_train, y_train)

# Evaluate the model with the best parameters
accuracy_best = clf_best.score(X_test, y_test)
print("Best accuracy:", accuracy_best)

In this code snippet, we defined a grid of hyperparameters to search over, created a grid search object using the GridSearchCV class, and fit the grid search to the training data. We then extracted the best hyperparameters using the best_params_ attribute and re-trained the model with the best hyperparameters. Finally, we evaluated the model with the best hyperparameters and printed the accuracy.

This tutorial covers the basics of machine learning with Scikit-Learn, including loading data, preprocessing data, building and evaluating models, and optimizing hyperparameters. Experiment with different datasets and machine learning algorithms to gain more experience and knowledge in the field of machine learning.