Challenge 4: Cross-validation

Cross-validation is a pivotal technique in machine learning that aims to assess the generalization performance of a model on unseen data. Given the inherent risk of overfitting a model to a particular dataset cross-validation offers a solution. By partitioning the original dataset into multiple subsets, the model is trained on some of these subsets and tested on the others.

By rotating the testing fold and averaging the results across all iterations, we gain a more robust estimate of the model's performance. This iterative process not only provides insights into the model's potential variability and bias but also aids in mitigating overfitting, ensuring that the model has a balanced performance across different subsets of the data.

Compito

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Implement a pipeline that combines data preprocessing and model training. After establishing the pipeline, utilize cross-validation to assess the performance of a classifier on the Wine dataset.

Create a pipeline that includes standard scaling and decision tree classifier.
Apply 5-fold cross-validation on the pipeline.
Calculate the average accuracy across all folds.

Soluzione

from sklearn.datasets import load_wine

from sklearn.pipeline import Pipeline

from sklearn.preprocessing import StandardScaler

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import cross_val_score

# Load Wine dataset

wine = load_wine()

X = wine.data

y = wine.target

# 1. Setup the pipeline

pipe = Pipeline([

('scaler', StandardScaler()),

('classifier', DecisionTreeClassifier(random_state=0))

])

# 2. Cross-validation

scores = cross_val_score(pipe, X, y, cv=5)

# 3. Average accuracy

average_accuracy = scores.mean()

print("Average Accuracy:", average_accuracy)

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Sezione 7. Capitolo 4