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Oppiskele Challenge: Classifying Unseparateble Data | Logistic Regression
Classification with Python

bookChallenge: Classifying Unseparateble Data

In this Challenge, you are given the following dataset:

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import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv') print(df.head())
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Here is its plot.

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import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv') plt.scatter(df['X1'], df['X2'], c=df['y'])
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The dataset is for sure not linearly separable. Let's look at the Logistic Regression performance:

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import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.model_selection import cross_val_score df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv') X = df[['X1', 'X2']] y = df['y'] X = StandardScaler().fit_transform(X) lr = LogisticRegression().fit(X, y) print(cross_val_score(lr, X, y).mean())
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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.

Tehtävä

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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.

Ratkaisu

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Completion rate improved to 3.57

bookChallenge: Classifying Unseparateble Data

Pyyhkäise näyttääksesi valikon

In this Challenge, you are given the following dataset:

1234
import pandas as pd df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv') print(df.head())
copy

Here is its plot.

12345
import pandas as pd import matplotlib.pyplot as plt df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv') plt.scatter(df['X1'], df['X2'], c=df['y'])
copy

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

123456789101112
import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.model_selection import cross_val_score df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv') X = df[['X1', 'X2']] y = df['y'] X = StandardScaler().fit_transform(X) lr = LogisticRegression().fit(X, y) print(cross_val_score(lr, X, y).mean())
copy

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.

Tehtävä

Swipe to start coding

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.

Ratkaisu

Switch to desktopVaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoista
Oliko kaikki selvää?

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Kiitos palautteestasi!

close

Awesome!

Completion rate improved to 3.57
Osio 2. Luku 6
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