Regression Example

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


X, y = evalml.demos.load_diabetes()

automl = AutoRegressionSearch(objective="R2", max_pipelines=5)

automl.search(X, y)

*****************************
* Beginning pipeline search *
*****************************

Optimizing for R2.
Greater score is better.

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

✔ Mean Baseline Regression Pipeline:         0%|          | Elapsed:00:00
✔ Cat Boost Regression Pipeline:            20%|██        | Elapsed:00:03
✔ Linear Regression Pipeline:               40%|████      | Elapsed:00:03
✔ Random Forest Regression Pipeline:        60%|██████    | Elapsed:00:04
✔ XGBoost Regression Pipeline:              80%|████████  | Elapsed:00:04
✔ Optimization finished                     80%|████████  | Elapsed:00:04

automl.rankings

	id	pipeline_name	score	high_variance_cv	parameters
0	2	Linear Regression Pipeline	0.488703	False	{'One Hot Encoder': {'top_n': 10}, 'Simple Imp...
1	3	Random Forest Regression Pipeline	0.447924	False	{'One Hot Encoder': {'top_n': 10}, 'Simple Imp...
2	1	Cat Boost Regression Pipeline	0.446477	False	{'Simple Imputer': {'impute_strategy': 'most_f...
3	4	XGBoost Regression Pipeline	0.331082	False	{'One Hot Encoder': {'top_n': 10}, 'Simple Imp...
4	0	Mean Baseline Regression Pipeline	-0.004217	False	{'strategy': 'mean'}

automl.best_pipeline

<evalml.pipelines.regression.linear_regression.LinearRegressionPipeline at 0x7f024ddd3a58>

automl.get_pipeline(0)

<evalml.pipelines.regression.baseline_regression.MeanBaselineRegressionPipeline at 0x7f024ddd3978>

automl.describe_pipeline(0)

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

Problem Type: Regression
Model Family: Baseline
Number of features: 10

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    294.000   148.000
1           -0.000                   79.654 68.759 6344.747    67.966   193.966        0.000    295.000   147.000
2           -0.006                   75.705 65.485 5731.187    63.817   170.817       -0.000    295.000   147.000
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          -         -