Regression Example

import evalml
from evalml import AutoMLSearch
from evalml.demos import load_diabetes
from evalml.pipelines import PipelineBase, get_pipelines


X, y = evalml.demos.load_diabetes()

automl = AutoMLSearch(problem_type='regression', objective="R2", max_pipelines=5)

automl.search(X, y)

Generating pipelines to search over...
*****************************
* Beginning pipeline search *
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Optimizing for R2.
Greater score is better.

Searching up to 5 pipelines.
Allowed model families: xgboost, catboost, linear_model, random_forest

✔ Mean Baseline Regression Pipeline:         0%|          | Elapsed:00:00
✔ CatBoost Regressor w/ Simple Imputer:     20%|██        | Elapsed:00:03
✔ Linear Regressor w/ Simple Imputer ...    40%|████      | Elapsed:00:03
✔ Random Forest Regressor w/ Simple I...    60%|██████    | Elapsed:00:04
✔ XGBoost Regressor w/ Simple Imputer:      80%|████████  | Elapsed:00:04
✔ Optimization finished                     80%|████████  | Elapsed:00:04

automl.rankings

	id	pipeline_name	score	high_variance_cv	parameters
0	2	Linear Regressor w/ Simple Imputer + Standard ...	0.488703	False	{'Simple Imputer': {'impute_strategy': 'most_f...
1	1	CatBoost Regressor w/ Simple Imputer	0.446477	False	{'Simple Imputer': {'impute_strategy': 'most_f...
2	3	Random Forest Regressor w/ Simple Imputer	0.441420	False	{'Simple Imputer': {'impute_strategy': 'most_f...
3	4	XGBoost Regressor w/ Simple Imputer	0.331082	False	{'Simple Imputer': {'impute_strategy': 'most_f...
4	0	Mean Baseline Regression Pipeline	-0.004217	False	{'Baseline Regressor': {'strategy': 'mean'}}

automl.best_pipeline

<evalml.pipelines.utils.make_pipeline.<locals>.GeneratedPipeline at 0x7fba225cb7d0>

automl.get_pipeline(0)

<evalml.pipelines.regression.baseline_regression.MeanBaselineRegressionPipeline at 0x7fba2153c450>

automl.describe_pipeline(0)

*************************************
* Mean Baseline Regression Pipeline *
*************************************

Problem Type: Regression
Model Family: Baseline

Pipeline Steps
==============
1. Baseline Regressor
         * strategy : mean

Training
========
Training for Regression problems.
Total training time (including CV): 0.0 seconds

Cross Validation
----------------
                R2  Root Mean Squared Error    MAE      MSE  MedianAE  MaxError  ExpVariance # Training # Testing
0           -0.007                   75.863 63.324 5755.216    57.190   186.810       -0.000      294.0     148.0
1           -0.000                   79.654 68.759 6344.747    67.966   193.966        0.000      295.0     147.0
2           -0.006                   75.705 65.485 5731.187    63.817   170.817       -0.000      295.0     147.0
mean        -0.004                   77.074 65.856 5943.717    62.991   183.864       -0.000          -         -
std          0.004                    2.236  2.736  347.510     5.435    11.852        0.000          -         -
coef of var -0.866                    0.029  0.042    0.058     0.086     0.064       -0.866          -         -