# Start¶

In this guide, we’ll show how you can use EvalML to automatically find the best pipeline for predicting whether a patient has breast cancer. Along the way, we’ll highlight EvalML’s built-in tools and features for understanding and interacting with the search process.

[1]:

import evalml
from evalml import AutoMLSearch


First, we load in the features and outcomes we want to use to train our model.

[2]:

X, y = evalml.demos.load_breast_cancer()


EvalML has many options to configure the pipeline search. At the minimum, we need to define an objective function. For simplicity, we will use the F1 score in this example. However, the real power of EvalML is in using domain-specific objective functions or building your own.

Below EvalML utilizes Bayesian optimization (EvalML’s default optimizer) to search and find the best pipeline defined by the given objective.

[3]:

automl = AutoMLSearch(problem_type="binary", objective="f1", max_pipelines=5)


In order to validate the results of the pipeline creation and optimization process, we will save some of our data as a holdout set.

[4]:

X_train, X_holdout, y_train, y_holdout = evalml.preprocessing.split_data(X, y, test_size=.2)


When we call search(), the search for the best pipeline will begin. There is no need to wrangle with missing data or categorical variables as EvalML includes various preprocessing steps (like imputation, one-hot encoding, feature selection) to ensure you’re getting the best results. As long as your data is in a single table, EvalML can handle it. If not, you can reduce your data to a single table by utilizing Featuretools and its Entity Sets.

You can find more information on pipeline components and how to integrate your own custom pipelines into EvalML here.

[5]:

automl.search(X_train, y_train)

Generating pipelines to search over...
*****************************
* Beginning pipeline search *
*****************************

Optimizing for F1.
Greater score is better.

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


(1/5) Mode Baseline Binary Classification P... Elapsed:00:00
Starting cross validation
Finished cross validation - mean F1: 0.770
(2/5) CatBoost Classifier w/ Simple Imputer    Elapsed:00:00
Starting cross validation
Finished cross validation - mean F1: 0.972
(3/5) XGBoost Classifier w/ Simple Imputer     Elapsed:00:41
Starting cross validation
Finished cross validation - mean F1: 0.967
(4/5) Random Forest Classifier w/ Simple Im... Elapsed:00:45
Starting cross validation
Finished cross validation - mean F1: 0.959
(5/5) Logistic Regression Classifier w/ Sim... Elapsed:00:47
Starting cross validation
Finished cross validation - mean F1: 0.984

Search finished after 00:49
Best pipeline: Logistic Regression Classifier w/ Simple Imputer + Standard Scaler
Best pipeline F1: 0.984311


After the search is finished we can view all of the pipelines searched, ranked by score. Internally, EvalML performs cross validation to score the pipelines. If it notices a high variance across cross validation folds, it will warn you. EvalML also provides additional data checks to analyze your data to assist you in producing the best performing pipeline.

[6]:

automl.rankings

[6]:

id pipeline_name score high_variance_cv parameters
0 4 Logistic Regression Classifier w/ Simple Imput... 0.984311 False {'Simple Imputer': {'impute_strategy': 'most_f...
1 1 CatBoost Classifier w/ Simple Imputer 0.972001 False {'Simple Imputer': {'impute_strategy': 'most_f...
2 2 XGBoost Classifier w/ Simple Imputer 0.966519 False {'Simple Imputer': {'impute_strategy': 'most_f...
3 3 Random Forest Classifier w/ Simple Imputer 0.959321 False {'Simple Imputer': {'impute_strategy': 'most_f...
4 0 Mode Baseline Binary Classification Pipeline 0.770273 False {'Baseline Classifier': {'strategy': 'random_w...

If we are interested in see more details about the pipeline, we can view a summary description using the id from the rankings table:

[7]:

automl.describe_pipeline(3)

**********************************************
* Random Forest Classifier w/ Simple Imputer *
**********************************************

Problem Type: Binary Classification
Model Family: Random Forest

Pipeline Steps
==============
1. Simple Imputer
* impute_strategy : most_frequent
* fill_value : None
2. Random Forest Classifier
* n_estimators : 100
* max_depth : 6
* n_jobs : -1

Training
========
Training for Binary Classification problems.
Total training time (including CV): 1.8 seconds

Cross Validation
----------------
F1  Accuracy Binary  Balanced Accuracy Binary  Precision   AUC  Log Loss Binary  MCC Binary # Training # Testing
0           0.952            0.941                     0.942      0.967 0.990            0.127       0.876    303.000   152.000
1           0.947            0.934                     0.930      0.947 0.973            0.363       0.860    303.000   152.000
2           0.979            0.974                     0.975      0.989 0.998            0.096       0.944    304.000   151.000
mean        0.959            0.950                     0.949      0.968 0.987            0.195       0.893          -         -
std         0.017            0.021                     0.024      0.021 0.012            0.146       0.045          -         -
coef of var 0.018            0.022                     0.025      0.022 0.012            0.746       0.050          -         -


We can also view the pipeline parameters directly:

[8]:

pipeline = automl.get_pipeline(3)
print(pipeline.parameters)

{'Simple Imputer': {'impute_strategy': 'most_frequent', 'fill_value': None}, 'Random Forest Classifier': {'n_estimators': 100, 'max_depth': 6, 'n_jobs': -1}}


We can now select the best pipeline and score it on our holdout data:

[9]:

pipeline = automl.best_pipeline
pipeline.fit(X_train, y_train)
pipeline.score(X_holdout, y_holdout, ["f1"])

[9]:

OrderedDict([('F1', 0.9795918367346939)])


We can also visualize the structure of the components contained by the pipeline:

[10]:

pipeline.graph()

[10]: