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Classification with Python

Challenge: Classifying Unseparateble DataChallenge: 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

  1. Create a pipeline to make an X_poly variable that will hold the polynomial features of degree 2 of X and be scaled.
  2. 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.
  3. Initialize and train a GridSearchCV object.

Everything was clear?

Section 2. Chapter 6
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course content

Course Content

Classification with Python

1. k-NN Classifier

What is ClassificationWhat is k-NNk-NN with Multiple FeaturesImplementing k-NNMulti-Class ClassificationTrain-test Split. Cross ValidationChallenge: Choosing the Best K Value.k-NN Summary

2. Logistic Regression

What is Logistic RegressionFinding the ParametersChallenge: Implementing Logistic RegressionDecision BoundaryOverfitting. RegularizationChallenge: Classifying Unseparateble DataLogistic Regression Summary

3. Decision Tree

What is Decision TreeSplitting the NodesCrazy TreeChallenge: Implementing a Decision TreeDecision Tree Summary

4. Random Forest

What is Random ForestThe Randomness of ForestChallenge: Implementing a Random ForestRandom Forest Summary

5. Comparing Models

Confusion MatrixMetricsChallenge: Comparing ModelsSummary

Classification with Python

Challenge: Classifying Unseparateble DataChallenge: 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

  1. Create a pipeline to make an X_poly variable that will hold the polynomial features of degree 2 of X and be scaled.
  2. 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.
  3. Initialize and train a GridSearchCV object.

Everything was clear?

Section 2. Chapter 6
toggle bottom row
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