PCA trains a model to project vectors to a lower dimensional space of the top k principal components. OneHotEncoder(*)Ī one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index. Normalize a vector to have unit norm using the given p-norm. Rescale each feature individually to a common range linearly using column summary statistics, which is also known as min-max normalization or Rescaling.Ī feature transformer that converts the input array of strings into an array of n-grams. Model produced by MinHashLSH, where where multiple hash functions are stored. Rescale each feature individually to range by dividing through the largest maximum absolute value in each feature. Implements the feature interaction transform.
IndexToString(*)Ī pyspark.ml.base.Transformer that maps a column of indices back to a new column of corresponding string values. Imputation estimator for completing missing values, either using the mean or the median of the columns in which the missing values are located. IDF(*)Ĭompute the Inverse Document Frequency (IDF) given a collection of documents. Maps a sequence of terms to their term frequencies using the hashing trick. Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided “weight” vector.įeatureHasher(*)įeature hashing projects a set of categorical or numerical features into a feature vector of specified dimension (typically substantially smaller than that of the original feature space). Maps a column of continuous features to a column of feature buckets.Ĭhi-Squared feature selection, which selects categorical features to use for predicting a categorical label.ĬountVectorizer(*)Įxtracts a vocabulary from document collections and generates a CountVectorizerModel.Ī feature transformer that takes the 1D discrete cosine transform of a real vector. Model fitted by BucketedRandomProjectionLSH, where multiple random vectors are stored.īucketizer(*) LSH class for Euclidean distance metrics.īucketedRandomProjectionLSHModel() Binarize a column of continuous features given a threshold.īucketedRandomProjectionLSH(*)
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