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
2020-08-06 20:10:29,398 featuretools - WARNING Featuretools failed to load plugin nlp_primitives from library nlp_primitives. For a full stack trace, set logging to debug.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/v0.12.2/lib/python3.7/site-packages/evalml/pipelines/components/transformers/preprocessing/text_featurizer.py:31: RuntimeWarning: No text columns were given to TextFeaturizer, component will have no effect warnings.warn("No text columns were given to TextFeaturizer, component will have no effect", RuntimeWarning)
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
true_positive
false_positive - dollar amount to be lost with an unsuccessful lead
false_positive
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 )
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', 'country'], axis=1) display(X.head())
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.
NaN
[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 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.
AutoMLSearch(problem_type='binary')
.search()
[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: linear_model, catboost, random_forest, xgboost
(1/5) Mode Baseline Binary Classification P... Elapsed:00:00 Starting cross validation Finished cross validation - mean Lead Scoring: 0.000 (2/5) CatBoost Classifier w/ Imputer Elapsed:00:02 Starting cross validation Finished cross validation - mean Lead Scoring: 29.276 (3/5) XGBoost Classifier w/ Imputer + One H... Elapsed:00:03 Starting cross validation Finished cross validation - mean Lead Scoring: 41.196 (4/5) Random Forest Classifier w/ Imputer +... Elapsed:01:17 Starting cross validation Finished cross validation - mean Lead Scoring: 40.865 (5/5) Logistic Regression Classifier w/ Imp... Elapsed:01:20 Starting cross validation Finished cross validation - mean Lead Scoring: 41.019 Search finished after 01:24 Best pipeline: XGBoost Classifier w/ Imputer + One Hot Encoder Best pipeline Lead Scoring: 41.196092
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
to select the best pipeline we can run
[7]:
best_pipeline = automl.best_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"])
*************************************************** * XGBoost Classifier w/ Imputer + One Hot Encoder * *************************************************** Problem Type: Binary Classification Model Family: XGBoost Pipeline Steps ============== 1. Imputer * categorical_impute_strategy : most_frequent * numeric_impute_strategy : mean * fill_value : None 2. One Hot Encoder * top_n : 10 * categories : None * drop : None * handle_unknown : ignore * handle_missing : error 3. XGBoost Classifier * eta : 0.1 * max_depth : 6 * min_child_weight : 1 * n_estimators : 100 Training ======== Training for Binary Classification problems. Objective to optimize binary classification pipeline thresholds for: <evalml.objectives.lead_scoring.LeadScoring object at 0x7fe645b06dd8> Total training time (including CV): 74.1 seconds Cross Validation ---------------- Lead Scoring AUC # Training # Testing 0 39.813 0.725 2479.000 1550.000 1 41.948 0.713 2479.000 1550.000 2 41.827 0.670 2480.000 1549.000 mean 41.196 0.703 - - std 1.199 0.029 - - coef of var 0.029 0.042 - -
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)
<evalml.pipelines.utils.make_pipeline.<locals>.GeneratedPipeline at 0x7fe6455fcf98>
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])
OrderedDict([('AUC', 0.6662964641885766), ('Lead Scoring', -0.051590713671539126)])
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: linear_model, catboost, random_forest, xgboost
(1/5) Mode Baseline Binary Classification P... Elapsed:00:00 Starting cross validation Finished cross validation - mean AUC: 0.500 (2/5) CatBoost Classifier w/ Imputer Elapsed:00:00 Starting cross validation Finished cross validation - mean AUC: 0.582 (3/5) XGBoost Classifier w/ Imputer + One H... Elapsed:00:00 Starting cross validation Finished cross validation - mean AUC: 0.728 (4/5) Random Forest Classifier w/ Imputer +... Elapsed:01:22 Starting cross validation Finished cross validation - mean AUC: 0.725 (5/5) Logistic Regression Classifier w/ Imp... Elapsed:01:23 Starting cross validation Finished cross validation - mean AUC: 0.700 Search finished after 01:24 Best pipeline: XGBoost Classifier w/ Imputer + One Hot Encoder Best pipeline AUC: 0.728289
like before, we can look at the rankings and pick the best pipeline
[12]:
automl_auc.rankings
[13]:
best_pipeline_auc = automl_auc.best_pipeline # train on the full training data best_pipeline_auc.fit(X_train, y_train)
<evalml.pipelines.utils.make_pipeline.<locals>.GeneratedPipeline at 0x7fe644c68cc0>
[14]:
# get the auc and lead scoring score on holdout data best_pipeline_auc.score(X_holdout, y_holdout, objectives=["auc", lead_scoring_objective])
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.
$7
$45
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.