Spark Rapids ML

Feature

PCA(*[, k, inputCol, outputCol, ...])	PCA algorithm learns principal component vectors to project high-dimensional vectors into low-dimensional vectors, while preserving the similarity of the vectors.
PCAModel(mean_, components_, ...)	Applies dimensionality reduction on an input DataFrame.

Classification

LogisticRegression(*[, featuresCol, ...])	LogisticRegression is a machine learning model where the response y is modeled by the sigmoid (or softmax for more than 2 classes) function applied to a linear combination of the features in X.
LogisticRegressionModel(coef_, intercept_, ...)	Model fitted by LogisticRegression.
RandomForestClassifier(*[, featuresCol, ...])	RandomForestClassifier implements a Random Forest classifier model which fits multiple decision tree classifiers in an ensemble.
RandomForestClassificationModel(n_cols, ...)	Model fitted by RandomForestClassifier.

Clustering

DBSCAN(*[, featuresCol, predictionCol, eps, ...])	The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a non-parametric data clustering algorithm based on data density.
DBSCANModel(n_cols, dtype)
KMeans(*[, featuresCol, predictionCol, k, ...])	KMeans algorithm partitions data points into a fixed number (denoted as k) of clusters.
KMeansModel(cluster_centers_, n_cols, dtype)	KMeans gpu model for clustering input vectors to learned k centers.

Regression

LinearRegression(*[, featuresCol, labelCol, ...])	LinearRegression is a machine learning model where the response y is modeled by a linear combination of the predictors in X.
LinearRegressionModel(coef_, intercept_, ...)	Model fitted by LinearRegression.
RandomForestRegressor(*[, featuresCol, ...])	RandomForestRegressor implements a Random Forest regressor model which fits multiple decision tree in an ensemble.
RandomForestRegressionModel(n_cols, dtype, ...)	Model fitted by RandomForestRegressor.

Nearest Neighbors

ApproximateNearestNeighbors(*[, k, ...])	ApproximateNearestNeighbors retrieves k approximate nearest neighbors (ANNs) in item vectors for each query.
ApproximateNearestNeighborsModel(item_df_withid)
NearestNeighbors(*[, k, inputCol, idCol, ...])	NearestNeighbors retrieves the exact k nearest neighbors in item vectors for each query vector.
NearestNeighborsModel(item_df_withid, ...)

Tuning

CrossValidator(*[, estimator, ...])

K-fold cross validation performs model selection by splitting the dataset into a set of non-overlapping randomly partitioned folds which are used as separate training and test datasets e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.

UMAP

UMAP(*[, n_neighbors, n_components, metric, ...])	Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique used for low-dimensional data visualization and general non-linear dimension reduction.
UMAPModel(embedding_, raw_data_, sparse_fit, ...)