Regression Example

[1]:
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 *
*****************************

Optimizing for R2.
Greater score is better.

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

✔ 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
[2]:
automl.rankings
[2]:
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'}}
[3]:
automl.best_pipeline
[3]:
<evalml.pipelines.utils.make_pipeline.<locals>.GeneratedPipeline at 0x7fd088fb8a20>
[4]:
automl.get_pipeline(0)
[4]:
<evalml.pipelines.regression.baseline_regression.MeanBaselineRegressionPipeline at 0x7fd088f8c748>
[5]:
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    294.000   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          -         -