EvalML
v0.9.0

Getting Started

  • What is EvalML
  • Installation

Objective functions

  • Overview
  • Fraud Prediction
  • Lead Scoring
  • Defining Custom Objectives

Automated Machine Learning

  • Setting up pipeline search
  • Exploring search results
  • Regression Example

Pipelines and Components

  • Pipelines
  • Components
  • Custom Pipelines

Guardrails

  • Overview
  • Overfitting Guardrails

Resources

  • Changelog
  • API Reference
    • Demo Datasets
    • Preprocessing
    • AutoML
      • evalml.automl.AutoClassificationSearch
      • evalml.automl.AutoRegressionSearch
        • evalml.automl.AutoRegressionSearch.__init__
        • evalml.automl.AutoRegressionSearch.describe_pipeline
        • evalml.automl.AutoRegressionSearch.get_pipeline
        • evalml.automl.AutoRegressionSearch.search
    • Pipelines
    • Components
    • Objective Functions
    • Problem Types
    • Model Family
    • Tuners
    • Guardrails
    • Utils
  • FAQ
EvalML
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  • evalml.automl.AutoRegressionSearch.search
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evalml.automl.AutoRegressionSearch.search¶

AutoRegressionSearch.search(X, y, feature_types=None, raise_errors=True, show_iteration_plot=True)¶

Find best classifier

Parameters
  • X (pd.DataFrame) – the input training data of shape [n_samples, n_features]

  • y (pd.Series) – the target training labels of length [n_samples]

  • feature_types (list, optional) – list of feature types, either numerical or categorical. Categorical features will automatically be encoded

  • raise_errors (boolean) – If True, raise errors and exit search if a pipeline errors during fitting. If False, set scores for the errored pipeline to NaN and continue search. Defaults to True.

  • show_iteration_plot (boolean, True) – Shows an iteration vs. score plot in Jupyter notebook. Disabled by default in non-Jupyter enviroments.

Returns

self

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