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Machine Learning Cross-Validation | Python Tutorials

Introduction

MindMap

In machine learning, cross-validation is a technique used to evaluate the performance of a model on an independent dataset. It helps to prevent overfitting by providing a better estimate of how well the model will generalize to new, unseen data.

In this lab, we will explore the concept of cross-validation and how to implement it using the scikit-learn library in Python.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Import the necessary libraries

First, let's import the necessary libraries for this lab.

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn import datasets
from sklearn import svm
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Load the dataset

Next, let's load a dataset to train our model on. In this example, we will use the Iris dataset, which is a popular dataset for classification tasks.

X, y = datasets.load_iris(return_X_y=True)
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Split the dataset into training and test sets

To evaluate the performance of our model, we need to split the dataset into a training set and a test set. We will use the train_test_split function from the scikit-learn library to do this.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
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Train and evaluate the model

Now, let's train a support vector machine (SVM) classifier on the training set and evaluate its performance on the test set.

clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
score = clf.score(X_test, y_test)
print("Accuracy: ", score)
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Summary

In this lab, we learned how to implement cross-validation using the scikit-learn library in Python. We split the dataset into training and test sets, trained a model on the training set, and evaluated its performance on the test set. Cross-validation helps to prevent overfitting and provides a better estimate of how well a model will generalize to new, unseen data.


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