LinearRegression

Ordinary least squares Linear Regression.

LogisticRegression

Logistic Regression (aka logit, MaxEnt) classifier.

GeminiTextGenerator

Gemini text generator LLM model.

PaLM2TextEmbeddingGenerator

PaLM2 text embedding generator LLM model.

PaLM2TextGenerator

PaLM2 text generator LLM model.

Pipeline

Pipeline of transforms with a final estimator.

KBinsDiscretizer

Bin continuous data into intervals.

LabelEncoder

Encode target labels with value between 0 and n_classes-1.

MaxAbsScaler

Scale each feature by its maximum absolute value.

MinMaxScaler

Transform features by scaling each feature to a given range.

OneHotEncoder

Encode categorical features as a one-hot format.

StandardScaler

Standardize features by removing the mean and scaling to unit variance.

VertexAIModel

Remote model from a Vertex AI https endpoint. User must specify https endpoint, input schema and output schema. How to deploy a model in Vertex AI https://cloud.google.com/bigquery/docs/bigquery-ml-remote-model-tutorial#Deploy-Model-on-Vertex-AI.

DatetimeMethods

Accessor object for datetime-like properties of the Series values.

PlotAccessor

Make plots of Series or DataFrame with the matplotlib backend.

StringMethods

Vectorized string functions for Series and Index.

StructAccessor

Accessor object for structured data properties of the Series values.

NamedAgg

NamedAgg(column, aggfunc)

option_context

Context manager to temporarily set options in the with statement context.

You need to invoke as option_context(pat, val, [(pat, val), ...]).

Series

N-dimensional analogue of DataFrame. Store multi-dimensional in a size-mutable, labeled data structure

Session

Establishes a BigQuery connection to capture a group of job activities related to DataFrames.

Modules

cluster

Clustering models. This module is styled after Scikit-Learn's cluster module: https://scikit-learn.org/stable/modules/clustering.html.

compose

Build composite transformers on heterogeneous data. This module is styled after scikit-Learn's compose module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.compose.

decomposition

Matrix Decomposition models. This module is styled after Scikit-Learn's decomposition module: https://scikit-learn.org/stable/modules/decomposition.html.

ensemble

Ensemble models. This module is styled after scikit-learn's ensemble module: https://scikit-learn.org/stable/modules/ensemble.html

forecasting

Forcasting models.

imported

Imported models.

linear_model

Linear models. This module is styled after scikit-learn's linear_model module: https://scikit-learn.org/stable/modules/linear_model.html.

llm

LLM models.

pairwise

API documentation for pairwise module.

model_selection

Functions for test/train split and model tuning. This module is styled after scikit-learn's model_selection module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection.

pipeline

For composing estimators together. This module is styled after scikit-learn's pipeline module: https://scikit-learn.org/stable/modules/pipeline.html.

preprocessing

Transformers that prepare data for other estimators. This module is styled after scikit-learn's preprocessing module: https://scikit-learn.org/stable/modules/preprocessing.html.

remote

BigFrames general remote models.

datetimes

API documentation for datetimes module.

plotting

API documentation for plotting module.

strings

API documentation for strings module.

structs

API documentation for structs module.