Encode target labels with value between 0 and n_classes-1. This transformer should be used to encode target values, i.e. Read more in the User Guide. New in version 0.12. Attributes:classes_ndarray of shape (n_classes,)Holds the label for each class. See also OrdinalEncoder Encode categorical features using an ordinal encoding scheme. OneHotEncoder Encode categorical features as a one-hot numeric array. Examples
>>> from sklearn import preprocessing >>> le = preprocessing.LabelEncoder() >>> le.fit([1, 2, 2, 6]) LabelEncoder() >>> le.classes_ array([1, 2, 6]) >>> le.transform([1, 1, 2, 6]) array([0, 0, 1, 2]...) >>> le.inverse_transform([0, 0, 1, 2]) array([1, 1, 2, 6]) It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. >>> le = preprocessing.LabelEncoder() >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) LabelEncoder() >>> list(le.classes_) ['amsterdam', 'paris', 'tokyo'] >>> le.transform(["tokyo", "tokyo", "paris"]) array([2, 2, 1]...) >>> list(le.inverse_transform([2, 2, 1])) ['tokyo', 'tokyo', 'paris'] Methods
Fit label encoder. Parameters:yarray-like of shape (n_samples,)Target values. Returns:selfreturns an instance of self.Fitted label encoder. fit_transform(y)[source]¶Fit label encoder and return encoded labels. Parameters:yarray-like of shape (n_samples,)Target values. Returns:yarray-like of shape (n_samples,)Encoded labels. get_params(deep=True)[source]¶Get parameters for this estimator. Parameters:deepbool, default=TrueIf True, will return the parameters for this estimator and contained subobjects that are estimators. Returns:paramsdictParameter names mapped to their values. Transform labels back to original encoding. Parameters:yndarray of shape (n_samples,)Target values. Returns:yndarray of shape (n_samples,)Original encoding. set_params(**params)[source]¶Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Estimator parameters. Returns:selfestimator instanceEstimator instance. transform(y)[source]¶Transform labels to normalized encoding. Parameters:yarray-like of shape (n_samples,)Target values. Returns:yarray-like of shape (n_samples,)Labels as normalized encodings. Kapan menggunakan label encoding?Terus kapan digunakan? Menggunakan label encoding ketika : Kategori dari data berbentuk ordinal atau memiliki rangking / hirarki urutan contohnya seperti kecil, sedang, besar, dan lain lain.
One hot encoding untuk apa?Bagi kamu yang belum tahu apa itu One-Hot-Encode, One-Hot-Encode adalah proses untuk membuat kolom baru dari variabel kategorikal kita di mana setiap kategori menjadi kolom baru dengan nilai 0 atau 1 (0 mewakili tidak ada dan 1 mewakili ada).
MinMaxScaler untuk apa?Tujuannya agar tahan terhadap pencilan data (outliers). MinMaxScaler juga biasa digunakan. Scaler tersebut membuat data berada pada rentang 0 -1. Selain itu,ada juga yang bisasa menggunakan Normalizer.
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