Challenge: Evaluating the Model with Cross-Validation

In this challenge, you will build and evaluate a model using both train-test evaluation and cross-validation. The data is an already preprocessed penguins dataset.

Here are some of the functions you will use:

Tâche

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Your task is to create a 4-nearest neighbors classifier and first evaluate its performance using the cross-validation score. Then split the data into train-test sets, train the model on the training set, and evaluate its performance on the test set.

Initialize a KNeighborsClassifier with 4 neighbors.
Calculate the cross-validation scores of this model with the number of folds set to 3. You can pass an untrained model to a cross_val_score() function.
Use a suitable function to split X, y.
Train the model using the training set.
Evaluate the model using the test set.

Solution

import pandas as pd

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import train_test_split, cross_val_score

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_pipelined.csv')

# Assign X, y variables (X is already preprocessed and y is already encoded)

X, y = df.drop('species', axis=1), df['species']

# Initialize a model

model = KNeighborsClassifier(n_neighbors=4)

# Calculate and print the mean of cross validation scores

scores = cross_val_score(model, X, y, cv=3)

print('Cross-val score:', scores.mean())

# Train-test split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# Train a model

model.fit(X_train, y_train)

# Print the score using the test set

print('Train-test score:', model.score(X_test, y_test))

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Section 4. Chapitre 5

import pandas as pd

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model_selection import train_test_split, cross_val_score

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins_pipelined.csv')

# Assign X, y variables (X is already preprocessed and y is already encoded)

X, y = df.drop('species', axis=1), df['species']

# Initialize a model

model = ___

# Calculate and print the mean of cross validation scores

scores = ___(___, X, y, cv=___)

print('Cross-val score:', scores.mean())

# Train-test split

X_train, X_test, y_train, y_test = ___(X, y, test_size=0.33)

# Train a model

___

# Print the score using the test set

print('Train-test score:', ___)

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