Course Content

# ML Introduction with scikit-learn

1. Machine Learning Concepts

2. Preprocessing Data with Scikit-learn

ML Introduction with scikit-learn

## Evaluate 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.

Some functions you will use:

# Task

Build a 4-nearest neighbors classifier and evaluate its performance using the cross-validation score first, then split the data into train-test sets, train the model using the training set, and evaluate it using 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.

Note: 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.

Everything was clear?

Course Content

# ML Introduction with scikit-learn

1. Machine Learning Concepts

2. Preprocessing Data with Scikit-learn

ML Introduction with scikit-learn

## Evaluate 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.

Some functions you will use:

# Task

Build a 4-nearest neighbors classifier and evaluate its performance using the cross-validation score first, then split the data into train-test sets, train the model using the training set, and evaluate it using 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.

Note: 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.

Everything was clear?