始める無料で始める

Scaling II

You'll now apply a scaler to the dataset, which is available for you as environment.

Remember that Scaling helps the algorithm converge faster, and avoids having one dominant feature heavily influence the outcomes.

この演習はコースの一部です

Analyzing IoT Data in Python

コースを見る

演習の手順

  • Initialize a StandardScaler and store it as sc.
  • Fit the scaler to environment.
  • Scale environment and store the result as environ_scaled.
  • Convert the scaled data back to a DataFrame, using the same columns and index than the original DataFrame.

実践的なインタラクティブ演習

このサンプルコードを完成させて、この演習に挑戦してみましょう。

# Initialize StandardScaler
sc = ____()

# Fit the scaler
sc.fit(____)

# Transform the data
environ_scaled = ____.____(____)

# Convert scaled data to DataFrame
environ_scaled = pd.DataFrame(____, 
                              columns=____, 
                              index=____)
print(environ_scaled.head())
plot_unscaled_scaled(environment, environ_scaled)
コードを編集して実行