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

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'], axis=1)

display(X.head())
job country state zip action amount
0 Engineer, mining NaN NY 60091.0 page_view NaN
1 Psychologist, forensic US CA NaN purchase 135.23
2 Psychologist, forensic US CA NaN page_view NaN
3 Air cabin crew US NaN 60091.0 download NaN
4 Air cabin crew US NaN 60091.0 page_view NaN

Search for 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.

[4]:
X_train, X_holdout, y_train, y_holdout = evalml.preprocessing.split_data(X, y, test_size=0.2, random_state=0)

print(X.dtypes)
job         object
country     object
state       object
zip        float64
action      object
amount     float64
dtype: object

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

[5]:
automl = AutoMLSearch(problem_type='binary',
                      objective=lead_scoring_objective,
                      additional_objectives=['auc'],
                      max_pipelines=5,
                      optimize_thresholds=True)

automl.search(X_train, y_train)
Generating pipelines to search over...
*****************************
* Beginning pipeline search *
*****************************

Optimizing for Lead Scoring.
Greater score is better.

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

✔ Mode Baseline Binary Classification...     0%|          | Elapsed:00:00
✔ CatBoost Classifier w/ Simple Imput...    20%|██        | Elapsed:00:11
✔ Logistic Regression Classifier w/ S...    40%|████      | Elapsed:00:13
✔ Random Forest Classifier w/ Simple ...    60%|██████    | Elapsed:00:16
✔ XGBoost Classifier w/ Simple Impute...    80%|████████  | Elapsed:00:17
✔ Optimization finished                     80%|████████  | Elapsed:00:17

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

[6]:
automl.rankings
[6]:
id pipeline_name score high_variance_cv parameters
0 1 CatBoost Classifier w/ Simple Imputer 44.280483 False {'Simple Imputer': {'impute_strategy': 'most_f...
1 0 Mode Baseline Binary Classification Pipeline 42.140250 False {'Baseline Classifier': {'strategy': 'random_w...
2 4 XGBoost Classifier w/ Simple Imputer + One Hot... 41.912311 False {'Simple Imputer': {'impute_strategy': 'most_f...
3 3 Random Forest Classifier w/ Simple Imputer + O... 41.507849 False {'Simple Imputer': {'impute_strategy': 'most_f...
4 2 Logistic Regression Classifier w/ Simple Imput... 41.402202 False {'Simple Imputer': {'impute_strategy': 'most_f...

to select the best pipeline we can run

[7]:
best_pipeline = automl.best_pipeline

Describe pipeline

You can get more details about any pipeline. Including how it performed on other objective functions.

[8]:
automl.describe_pipeline(automl.rankings.iloc[0]["id"])
*****************************************
* CatBoost Classifier w/ Simple Imputer *
*****************************************

Problem Type: Binary Classification
Model Family: CatBoost

Pipeline Steps
==============
1. Simple Imputer
         * impute_strategy : most_frequent
         * fill_value : None
2. CatBoost Classifier
         * n_estimators : 1000
         * eta : 0.03
         * max_depth : 6
         * bootstrap_type : None

Training
========
Training for Binary Classification problems.
Objective to optimize binary classification pipeline thresholds for: <evalml.objectives.lead_scoring.LeadScoring object at 0x7f2a962c4898>
Total training time (including CV): 11.0 seconds

Cross Validation
----------------
             Lead Scoring   AUC # Training # Testing
0                  45.845 0.925   2479.000  1550.000
1                  42.748 0.922   2479.000  1550.000
2                  44.248 0.937   2480.000  1549.000
mean               44.280 0.928          -         -
std                 1.549 0.008          -         -
coef of var         0.035 0.008          -         -

Evaluate on hold out

Finally, we retrain the best pipeline on all of the training data and evaluate on the holdout

[9]:
best_pipeline.fit(X_train, y_train)
[9]:
<evalml.pipelines.utils.make_pipeline.<locals>.GeneratedPipeline at 0x7f2a962c4e80>

Now, we can score the pipeline on the hold out data using both the lead scoring score and the AUC.

[10]:
best_pipeline.score(X_holdout, y_holdout, objectives=["auc", lead_scoring_objective])
[10]:
OrderedDict([('AUC', 0.9402462979752191),
             ('Lead Scoring', 11.969045571797077)])

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(problem_type='binary',
                                 objective='auc',
                                 additional_objectives=[],
                                 max_pipelines=5,
                                 optimize_thresholds=True)

automl_auc.search(X_train, y_train)
Generating pipelines to search over...
*****************************
* Beginning pipeline search *
*****************************

Optimizing for AUC.
Greater score is better.

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

✔ Mode Baseline Binary Classification...     0%|          | Elapsed:00:00
✔ CatBoost Classifier w/ Simple Imput...    20%|██        | Elapsed:00:11
✔ Logistic Regression Classifier w/ S...    40%|████      | Elapsed:00:11
✔ Random Forest Classifier w/ Simple ...    60%|██████    | Elapsed:00:13
✔ XGBoost Classifier w/ Simple Impute...    80%|████████  | Elapsed:00:14
✔ Optimization finished                     80%|████████  | Elapsed:00:14

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

[12]:
automl_auc.rankings
[12]:
id pipeline_name score high_variance_cv parameters
0 1 CatBoost Classifier w/ Simple Imputer 0.931551 False {'Simple Imputer': {'impute_strategy': 'most_f...
1 4 XGBoost Classifier w/ Simple Imputer + One Hot... 0.723820 False {'Simple Imputer': {'impute_strategy': 'most_f...
2 3 Random Forest Classifier w/ Simple Imputer + O... 0.703700 False {'Simple Imputer': {'impute_strategy': 'most_f...
3 2 Logistic Regression Classifier w/ Simple Imput... 0.702138 False {'Simple Imputer': {'impute_strategy': 'most_f...
4 0 Mode Baseline Binary Classification Pipeline 0.500000 False {'Baseline Classifier': {'strategy': 'random_w...
[13]:
best_pipeline_auc = automl_auc.best_pipeline

# train on the full training data
best_pipeline_auc.fit(X_train, y_train)
[13]:
<evalml.pipelines.utils.make_pipeline.<locals>.GeneratedPipeline at 0x7f2a74575c18>
[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', 0.9402462979752191),
             ('Lead Scoring', 11.969045571797077)])

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.