# Challenge: Classifying Unseparateble Data

In this Challenge, you are given the following dataset:

Here is its plot.

The dataset is for sure not linearly separable. Let's look at the Logistic Regression performance:

The result is awful. Regular Logistic Regression is not suited for this task. Your task is to check whether the `PolynomialFeatures`

will help. To find the best `C`

parameter, you will use the `GridSearchCV`

class.

In this challenge, the `Pipeline`

is used. You can think of it as a list of preprocessing steps. Its `.fit_transform()`

method sequentially applies `.fit_transform()`

to each item.

Task

Build a Logistic Regression model with polynomial features and find the best `C`

parameter using `GridSearchCV`

- Create a pipeline to make an
`X_poly`

variable that will hold the polynomial features of degree 2 of`X`

and be scaled. - Create a
`param_grid`

dictionary to tell the`GridSearchCV`

you want to try values`[0.01, 0.1, 1, 10, 100]`

of a`C`

parameter. - Initialize and train a
`GridSearchCV`

object.

Everything was clear?

Course Content

Classification with Python

## Classification with Python

5. Comparing Models

# Challenge: Classifying Unseparateble Data

In this Challenge, you are given the following dataset:

Here is its plot.

The dataset is for sure not linearly separable. Let's look at the Logistic Regression performance:

The result is awful. Regular Logistic Regression is not suited for this task. Your task is to check whether the `PolynomialFeatures`

will help. To find the best `C`

parameter, you will use the `GridSearchCV`

class.

In this challenge, the `Pipeline`

is used. You can think of it as a list of preprocessing steps. Its `.fit_transform()`

method sequentially applies `.fit_transform()`

to each item.

Task

Build a Logistic Regression model with polynomial features and find the best `C`

parameter using `GridSearchCV`

- Create a pipeline to make an
`X_poly`

variable that will hold the polynomial features of degree 2 of`X`

and be scaled. - Create a
`param_grid`

dictionary to tell the`GridSearchCV`

you want to try values`[0.01, 0.1, 1, 10, 100]`

of a`C`

parameter. - Initialize and train a
`GridSearchCV`

object.

Everything was clear?