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 leadfalse_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: xgboost, linear_model, catboost, random_forest
✔ 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:00:16[21:05:25] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/v0.11.0/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning:
The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/v0.11.0/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning:
The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
[21:05:26] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/v0.11.0/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning:
The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
[21:05:26] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
✔ 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 | 2 | Logistic Regression Classifier w/ Simple Imput... | 41.972462 | False | {'Simple Imputer': {'impute_strategy': 'most_f... |
| 3 | 4 | XGBoost Classifier w/ Simple Imputer + One Hot... | 41.912311 | False | {'Simple Imputer': {'impute_strategy': 'most_f... |
| 4 | 3 | Random Forest Classifier w/ Simple Imputer + O... | 41.507849 | 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 0x7fae74318f10>
Total training time (including CV): 11.3 seconds
Cross Validation
----------------
Lead Scoring AUC # Training # Testing
0 45.845 0.925 2479.0 1550.0
1 42.748 0.922 2479.0 1550.0
2 44.248 0.937 2480.0 1549.0
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 0x7fae43dc93d0>
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: xgboost, linear_model, catboost, random_forest
✔ Mode Baseline Binary Classification... 0%| | Elapsed:00:00
✔ CatBoost Classifier w/ Simple Imput... 20%|██ | Elapsed:00:11
✔ Logistic Regression Classifier w/ S... 40%|████ | Elapsed:00:12
✔ Random Forest Classifier w/ Simple ... 60%|██████ | Elapsed:00:13
▹ XGBoost Classifier w/ Simple Impute... 80%|████████ | Elapsed:00:13[21:05:47] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/v0.11.0/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning:
The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/v0.11.0/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning:
The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
[21:05:47] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/v0.11.0/lib/python3.7/site-packages/xgboost/sklearn.py:888: UserWarning:
The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].
[21:05:48] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
✔ 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... | 0.702151 | 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 0x7fae40e359d0>
[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.