Contenido del Curso
ML Introduction with scikit-learn
ML Introduction with scikit-learn
1. Machine Learning Concepts
2. Preprocessing Data with Scikit-learn
Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesChallenge: Imputing Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderChallenge: Encoding Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerChallenge: Scaling the FeaturesPreprocessing Summary
LabelEncoder
The OrdinalEncoder and OneHotEncoder are typically used to encode features (the X variable). However, the target variable (y) can also be categorical.
import pandas as pd # Load the data and assign X, y variables df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/adult_edu.csv') y = df['income'] # Income is a target in this dataset X = df.drop('income', axis=1) print(y) print('All values: ', y.unique())
The LabelEncoder is used to encode the target, regardless of whether it is nominal or ordinal.
ML models do not consider the order of the target, allowing it to be encoded as any numerical values.
LabelEncoder encodes the target to numbers 0, 1, ... .
import pandas as pd from sklearn.preprocessing import LabelEncoder # Load the data and assign X, y variables df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/adult_edu.csv') y = df['income'] # Income is a target in this dataset X = df.drop('income', axis=1) # Initialize a LabelEncoder object and encode the y variable label_enc = LabelEncoder() y = label_enc.fit_transform(y) print(y) # Decode the y variable back y_decoded = label_enc.inverse_transform(y) print(y_decoded)
The code above encodes the target using LabelEncoder and then uses the .inverse_transform() method to convert it back to the original representation.
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Sección 2. Capítulo 7
