Building a Lead Scoring Model with EvalML¶

In this demo, we will build an optimized lead scoring model using EvalML. To optimize the pipeline, we will set up an objective function to maximize the revenue generated with true positives while taking into account the cost of false positives. At the end of this demo, we also show you how introducing the right objective during the training is over 6x better than using a generic machine learning metric like AUC.

[1]:

import evalml
from evalml import AutoMLSearch
from evalml.objectives import LeadScoring

Configure LeadScoring¶

To optimize the pipelines toward the specific business needs of this model, you can set your own assumptions for how much value is gained through true positives and the cost associated with false positives. These parameters are

true_positive - dollar amount to be gained with a successful lead
false_positive - dollar amount to be lost with an unsuccessful lead

Using these parameters, EvalML builds a pileline that will maximize the amount of revenue per lead generated.

[2]:

lead_scoring_objective = LeadScoring(
    true_positives=1000,
    false_positives=-10
)

Dataset¶

We will be utilizing a dataset detailing a customer’s job, country, state, zip, online action, the dollar amount of that action and whether they were a successful lead.

[3]:

from urllib.request import urlopen
import pandas as pd
import woodwork as ww
customers_data = urlopen('https://featurelabs-static.s3.amazonaws.com/lead_scoring_ml_apps/customers.csv')
interactions_data = urlopen('https://featurelabs-static.s3.amazonaws.com/lead_scoring_ml_apps/interactions.csv')
leads_data = urlopen('https://featurelabs-static.s3.amazonaws.com/lead_scoring_ml_apps/previous_leads.csv')
customers = pd.read_csv(customers_data)
interactions = pd.read_csv(interactions_data)
leads = pd.read_csv(leads_data)

X = customers.merge(interactions, on='customer_id').merge(leads, on='customer_id')
y = X['label']
X = X.drop(['customer_id', 'date_registered', 'birthday','phone', 'email',
            'owner', 'company', 'id', 'time_x',
            'session', 'referrer', 'time_y', 'label', 'country'], axis=1)
display(X.head())

	job	state	zip	action	amount
0	Engineer, mining	NY	60091.0	page_view	NaN
1	Psychologist, forensic	CA	NaN	purchase	135.23
2	Psychologist, forensic	CA	NaN	page_view	NaN
3	Air cabin crew	NaN	60091.0	download	NaN
4	Air cabin crew	NaN	60091.0	page_view	NaN

We will convert our data into Woodwork data structures. Doing so enables us to have more control over the types passed to and inferred by AutoML.

[4]:

X = ww.DataTable(X, semantic_tags={'job': 'category'}, logical_types={'job': 'Categorical'})
y = ww.DataColumn(y)
X.types

[4]:

	Physical Type	Logical Type	Semantic Tag(s)
Data Column
job	category	Categorical	['category']
state	category	Categorical	['category']
zip	float64	Double	['numeric']
action	category	Categorical	['category']
amount	float64	Double	['numeric']

Search for the best pipeline¶

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

EvalML natively supports one-hot encoding and imputation so the above NaN and categorical values will be taken care of.

[5]:

X_train, X_holdout, y_train, y_holdout = evalml.preprocessing.split_data(X, y, problem_type='binary', test_size=0.2, random_seed=0)

print(X.types)

            Physical Type Logical Type Semantic Tag(s)
Data Column
job              category  Categorical    ['category']
state            category  Categorical    ['category']
zip               float64       Double     ['numeric']
action           category  Categorical    ['category']
amount            float64       Double     ['numeric']

Because the lead scoring labels are binary, we will use AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary'). When we call .search(), the search for the best pipeline will begin.

[6]:

automl = AutoMLSearch(X_train=X_train, y_train=y_train,
                      problem_type='binary',
                      objective=lead_scoring_objective,
                      additional_objectives=['auc'],
                      max_batches=1,
                      optimize_thresholds=True)

automl.search()

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

Optimizing for Lead Scoring.
Greater score is better.

Using SequentialEngine to train and score pipelines.
Searching up to 1 batches for a total of 9 pipelines.
Allowed model families: xgboost, extra_trees, random_forest, linear_model, decision_tree, catboost, lightgbm

Batch 1: (1/9) Mode Baseline Binary Classification P... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean Lead Scoring: 0.000
Batch 1: (2/9) Decision Tree Classifier w/ Imputer +... Elapsed:00:01
        Starting cross validation

/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/v0.21.0/lib/python3.8/site-packages/evalml/automl/automl_search.py:703: RuntimeWarning:

invalid value encountered in double_scalars

        Finished cross validation - mean Lead Scoring: 11.362
High coefficient of variation (cv >= 0.2) within cross validation scores. Decision Tree Classifier w/ Imputer + One Hot Encoder may not perform as estimated on unseen data.
Batch 1: (3/9) LightGBM Classifier w/ Imputer + One ... Elapsed:00:02
        Starting cross validation
        Finished cross validation - mean Lead Scoring: 11.979
High coefficient of variation (cv >= 0.2) within cross validation scores. LightGBM Classifier w/ Imputer + One Hot Encoder may not perform as estimated on unseen data.
Batch 1: (4/9) Extra Trees Classifier w/ Imputer + O... Elapsed:00:03
        Starting cross validation
        Finished cross validation - mean Lead Scoring: 7.296
High coefficient of variation (cv >= 0.2) within cross validation scores. Extra Trees Classifier w/ Imputer + One Hot Encoder may not perform as estimated on unseen data.
Batch 1: (5/9) Elastic Net Classifier w/ Imputer + O... Elapsed:00:04
        Starting cross validation
        Finished cross validation - mean Lead Scoring: 15.997
Batch 1: (6/9) CatBoost Classifier w/ Imputer           Elapsed:00:05
        Starting cross validation
        Finished cross validation - mean Lead Scoring: 8.097
High coefficient of variation (cv >= 0.2) within cross validation scores. CatBoost Classifier w/ Imputer may not perform as estimated on unseen data.
Batch 1: (7/9) XGBoost Classifier w/ Imputer + One H... Elapsed:00:06
        Starting cross validation
        Finished cross validation - mean Lead Scoring: 14.360
Batch 1: (8/9) Random Forest Classifier w/ Imputer +... Elapsed:00:07
        Starting cross validation
        Finished cross validation - mean Lead Scoring: 7.720
High coefficient of variation (cv >= 0.2) within cross validation scores. Random Forest Classifier w/ Imputer + One Hot Encoder may not perform as estimated on unseen data.
Batch 1: (9/9) Logistic Regression Classifier w/ Imp... Elapsed:00:09
        Starting cross validation
        Finished cross validation - mean Lead Scoring: 8.412
High coefficient of variation (cv >= 0.2) within cross validation scores. Logistic Regression Classifier w/ Imputer + One Hot Encoder + Standard Scaler may not perform as estimated on unseen data.

Search finished after 00:11
Best pipeline: Elastic Net Classifier w/ Imputer + One Hot Encoder + Standard Scaler
Best pipeline Lead Scoring: 15.996914

View rankings and select pipeline¶

Once the fitting process is done, we can see all of the pipelines that were searched, ranked by their score on the lead scoring objective we defined.

[7]:

automl.rankings

[7]:

	id	pipeline_name	score	validation_score	percent_better_than_baseline	high_variance_cv	parameters
0	4	Elastic Net Classifier w/ Imputer + One Hot En...	15.996914	16.741935	inf	False	{'Imputer': {'categorical_impute_strategy': 'm...
1	6	XGBoost Classifier w/ Imputer + One Hot Encoder	14.360038	13.858065	inf	False	{'Imputer': {'categorical_impute_strategy': 'm...
2	2	LightGBM Classifier w/ Imputer + One Hot Encoder	11.979450	8.470968	inf	True	{'Imputer': {'categorical_impute_strategy': 'm...
3	1	Decision Tree Classifier w/ Imputer + One Hot ...	11.361864	7.612903	inf	True	{'Imputer': {'categorical_impute_strategy': 'm...
4	8	Logistic Regression Classifier w/ Imputer + On...	8.411923	3.916129	inf	True	{'Imputer': {'categorical_impute_strategy': 'm...
5	5	CatBoost Classifier w/ Imputer	8.097116	5.961290	inf	True	{'Imputer': {'categorical_impute_strategy': 'm...
6	7	Random Forest Classifier w/ Imputer + One Hot ...	7.720320	8.587097	inf	True	{'Imputer': {'categorical_impute_strategy': 'm...
7	3	Extra Trees Classifier w/ Imputer + One Hot En...	7.295755	3.296774	inf	True	{'Imputer': {'categorical_impute_strategy': 'm...
8	0	Mode Baseline Binary Classification Pipeline	0.000000	0.000000	0.0	False	{'Baseline Classifier': {'strategy': 'mode'}}

To select the best pipeline we can call automl.best_pipeline.

[8]:

best_pipeline = automl.best_pipeline

Describe pipeline¶

You can get more details about any pipeline, including how it performed on other objective functions by calling .describe_pipeline() and specifying the id of the pipeline.

[9]:

automl.describe_pipeline(automl.rankings.iloc[0]["id"])

*************************************************************************
* Elastic Net Classifier w/ Imputer + One Hot Encoder + Standard Scaler *
*************************************************************************

Problem Type: binary
Model Family: Linear

Pipeline Steps
==============
1. Imputer
         * categorical_impute_strategy : most_frequent
         * numeric_impute_strategy : mean
         * categorical_fill_value : None
         * numeric_fill_value : None
2. One Hot Encoder
         * top_n : 10
         * features_to_encode : None
         * categories : None
         * drop : if_binary
         * handle_unknown : ignore
         * handle_missing : error
3. Standard Scaler
4. Elastic Net Classifier
         * alpha : 0.5
         * l1_ratio : 0.5
         * n_jobs : -1
         * max_iter : 1000
         * penalty : elasticnet
         * loss : log

Training
========
Training for binary problems.
Objective to optimize binary classification pipeline thresholds for: <evalml.objectives.lead_scoring.LeadScoring object at 0x7fe4ded56340>
Total training time (including CV): 1.2 seconds

Cross Validation
----------------
             Lead Scoring   AUC # Training # Validation
0                  16.742 0.500      800.0       1550.0
1                  15.600 0.500      800.0       1550.0
2                  15.649 0.500      800.0       1549.0
mean               15.997 0.500          -            -
std                 0.646 0.000          -            -
coef of var         0.040 0.000          -            -

Evaluate on hold out¶

Finally, since the best pipeline was trained on all of the training data, we evaluate it on the holdout dataset.

[10]:

best_pipeline.score(X_holdout, y_holdout, objectives=["auc", lead_scoring_objective])

[10]:

OrderedDict([('AUC', 0.5), ('Lead Scoring', 15.382631126397248)])

Why optimize for a problem-specific objective?¶

To demonstrate the importance of optimizing for the right objective, let’s search for another pipeline using AUC, a common machine learning metric. After that, we will score the holdout data using the lead scoring objective to see how the best pipelines compare.

[11]:

automl_auc = evalml.AutoMLSearch(X_train=X_train, y_train=y_train,
                                 problem_type='binary',
                                 objective='auc',
                                 additional_objectives=[],
                                 max_batches=1,
                                 optimize_thresholds=True)

automl_auc.search()

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

Optimizing for AUC.
Greater score is better.

Using SequentialEngine to train and score pipelines.
Searching up to 1 batches for a total of 9 pipelines.
Allowed model families: xgboost, extra_trees, random_forest, linear_model, decision_tree, catboost, lightgbm

Batch 1: (1/9) Mode Baseline Binary Classification P... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean AUC: 0.500
Batch 1: (2/9) Decision Tree Classifier w/ Imputer +... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean AUC: 0.623
Batch 1: (3/9) LightGBM Classifier w/ Imputer + One ... Elapsed:00:00
        Starting cross validation
        Finished cross validation - mean AUC: 0.689
Batch 1: (4/9) Extra Trees Classifier w/ Imputer + O... Elapsed:00:01
        Starting cross validation
        Finished cross validation - mean AUC: 0.691
Batch 1: (5/9) Elastic Net Classifier w/ Imputer + O... Elapsed:00:02
        Starting cross validation
        Finished cross validation - mean AUC: 0.500
Batch 1: (6/9) CatBoost Classifier w/ Imputer           Elapsed:00:02
        Starting cross validation
        Finished cross validation - mean AUC: 0.606
Batch 1: (7/9) XGBoost Classifier w/ Imputer + One H... Elapsed:00:03
        Starting cross validation
        Finished cross validation - mean AUC: 0.731
Batch 1: (8/9) Random Forest Classifier w/ Imputer +... Elapsed:00:03
        Starting cross validation
        Finished cross validation - mean AUC: 0.701
Batch 1: (9/9) Logistic Regression Classifier w/ Imp... Elapsed:00:04
        Starting cross validation
        Finished cross validation - mean AUC: 0.662

Search finished after 00:05
Best pipeline: XGBoost Classifier w/ Imputer + One Hot Encoder
Best pipeline AUC: 0.731004

[12]:

automl_auc.rankings

[12]:

	id	pipeline_name	score	validation_score	percent_better_than_baseline	high_variance_cv	parameters
0	6	XGBoost Classifier w/ Imputer + One Hot Encoder	0.731004	0.741786	23.100374	False	{'Imputer': {'categorical_impute_strategy': 'm...
1	7	Random Forest Classifier w/ Imputer + One Hot ...	0.700766	0.732207	20.076573	False	{'Imputer': {'categorical_impute_strategy': 'm...
2	3	Extra Trees Classifier w/ Imputer + One Hot En...	0.690552	0.727024	19.055172	False	{'Imputer': {'categorical_impute_strategy': 'm...
3	2	LightGBM Classifier w/ Imputer + One Hot Encoder	0.688632	0.696905	18.863192	False	{'Imputer': {'categorical_impute_strategy': 'm...
4	8	Logistic Regression Classifier w/ Imputer + On...	0.661521	0.659379	16.152107	False	{'Imputer': {'categorical_impute_strategy': 'm...
5	1	Decision Tree Classifier w/ Imputer + One Hot ...	0.623376	0.673780	12.337568	False	{'Imputer': {'categorical_impute_strategy': 'm...
6	5	CatBoost Classifier w/ Imputer	0.606492	0.660816	10.649198	False	{'Imputer': {'categorical_impute_strategy': 'm...
7	0	Mode Baseline Binary Classification Pipeline	0.500000	0.500000	0.000000	False	{'Baseline Classifier': {'strategy': 'mode'}}
8	4	Elastic Net Classifier w/ Imputer + One Hot En...	0.500000	0.500000	0.000000	False	{'Imputer': {'categorical_impute_strategy': 'm...

Like before, we can look at the rankings and pick the best pipeline.

[13]:

best_pipeline_auc = automl_auc.best_pipeline

[14]:

# get the auc and lead scoring score on holdout data
best_pipeline_auc.score(X_holdout, y_holdout,  objectives=["auc", lead_scoring_objective])

[14]:

OrderedDict([('AUC', 0.7007932910244787),
             ('Lead Scoring', 0.7136715391229579)])

When we optimize for AUC, we can see that the AUC score from this pipeline is better than the AUC score from the pipeline optimized for lead scoring. However, the revenue per lead gained was only $7 per lead when optimized for AUC and was $45 when optimized for lead scoring. As a result, we would gain up to 6x the amount of revenue if we optimized for lead scoring.

This happens because optimizing for AUC does not take into account the user-specified true_positive (dollar amount to be gained with a successful lead) and false_positive (dollar amount to be lost with an unsuccessful lead) values. Thus, the best pipelines may produce the highest AUC but may not actually generate the most revenue through lead scoring.

This example highlights how performance in the real world can diverge greatly from machine learning metrics.

Building a Fraud Prediction Model with EvalML Using the Cost-Benefit Matrix Objective