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
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 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())
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
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
[5]:
X_train, X_holdout, y_train, y_holdout = evalml.preprocessing.split_data(X, y, problem_type='binary', test_size=0.2, random_state=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.
AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')
.search()
[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()
The following labels fall below 10% of the target: [True] Generating pipelines to search over... ***************************** * Beginning pipeline search * ***************************** Optimizing for Lead Scoring. Greater score is better. Searching up to 1 batches for a total of 9 pipelines. Allowed model families: catboost, decision_tree, random_forest, linear_model, extra_trees, lightgbm, xgboost
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) Logistic Regression Classifier w/ Imp... Elapsed:00:01 Starting cross validation Finished cross validation - mean Lead Scoring: 14.941 Batch 1: (3/9) Random Forest Classifier w/ Imputer +... Elapsed:00:04 Starting cross validation Finished cross validation - mean Lead Scoring: 14.515 Batch 1: (4/9) XGBoost Classifier w/ Imputer + One H... Elapsed:00:06 Starting cross validation Finished cross validation - mean Lead Scoring: 14.537 Batch 1: (5/9) CatBoost Classifier w/ Imputer Elapsed:00:09 Starting cross validation Finished cross validation - mean Lead Scoring: 15.997 Batch 1: (6/9) Elastic Net Classifier w/ Imputer + O... Elapsed:00:10 Starting cross validation Finished cross validation - mean Lead Scoring: 15.997 Batch 1: (7/9) Extra Trees Classifier w/ Imputer + O... Elapsed:00:12 Starting cross validation Finished cross validation - mean Lead Scoring: 15.294 Batch 1: (8/9) LightGBM Classifier w/ Imputer + One ... Elapsed:00:14 Starting cross validation Finished cross validation - mean Lead Scoring: 13.354 Batch 1: (9/9) Decision Tree Classifier w/ Imputer +... Elapsed:00:16 Starting cross validation Finished cross validation - mean Lead Scoring: 14.821 Search finished after 00:18 Best pipeline: CatBoost Classifier w/ Imputer Best pipeline Lead Scoring: 15.996914
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
To select the best pipeline we can call automl.best_pipeline.
automl.best_pipeline
[8]:
best_pipeline = automl.best_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.
.describe_pipeline()
id
[9]:
automl.describe_pipeline(automl.rankings.iloc[0]["id"])
********************************** * CatBoost Classifier w/ Imputer * ********************************** Problem Type: binary Model Family: CatBoost Pipeline Steps ============== 1. Imputer * categorical_impute_strategy : most_frequent * numeric_impute_strategy : mean * categorical_fill_value : None * numeric_fill_value : None 2. CatBoost Classifier * n_estimators : 10 * eta : 0.03 * max_depth : 6 * bootstrap_type : None * silent : True * allow_writing_files : False Training ======== Training for binary problems. Objective to optimize binary classification pipeline thresholds for: <evalml.objectives.lead_scoring.LeadScoring object at 0x7f1230b074c0> Total training time (including CV): 1.2 seconds Cross Validation ---------------- Lead Scoring AUC # Training # Validation 0 16.742 0.600 2479.000 1550.000 1 15.600 0.509 2479.000 1550.000 2 15.649 0.576 2480.000 1549.000 mean 15.997 0.562 - - std 0.646 0.047 - - coef of var 0.040 0.084 - -
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])
OrderedDict([('AUC', 0.559035962526443), ('Lead Scoring', 15.382631126397248)])
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()
The following labels fall below 10% of the target: [True] Generating pipelines to search over... ***************************** * Beginning pipeline search * ***************************** Optimizing for AUC. Greater score is better. Searching up to 1 batches for a total of 9 pipelines. Allowed model families: catboost, decision_tree, random_forest, linear_model, extra_trees, lightgbm, xgboost
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) Logistic Regression Classifier w/ Imp... Elapsed:00:00 Starting cross validation Finished cross validation - mean AUC: 0.681 Batch 1: (3/9) Random Forest Classifier w/ Imputer +... Elapsed:00:00 Starting cross validation Finished cross validation - mean AUC: 0.682 Batch 1: (4/9) XGBoost Classifier w/ Imputer + One H... Elapsed:00:02 Starting cross validation Finished cross validation - mean AUC: 0.701 Batch 1: (5/9) CatBoost Classifier w/ Imputer Elapsed:00:03 Starting cross validation Finished cross validation - mean AUC: 0.542 Batch 1: (6/9) Elastic Net Classifier w/ Imputer + O... Elapsed:00:03 Starting cross validation Finished cross validation - mean AUC: 0.500 Batch 1: (7/9) Extra Trees Classifier w/ Imputer + O... Elapsed:00:04 Starting cross validation Finished cross validation - mean AUC: 0.689 Batch 1: (8/9) LightGBM Classifier w/ Imputer + One ... Elapsed:00:05 Starting cross validation Finished cross validation - mean AUC: 0.671 Batch 1: (9/9) Decision Tree Classifier w/ Imputer +... Elapsed:00:06 Starting cross validation Finished cross validation - mean AUC: 0.594 Search finished after 00:07 Best pipeline: XGBoost Classifier w/ Imputer + One Hot Encoder Best pipeline AUC: 0.701032
[12]:
automl_auc.rankings
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])
OrderedDict([('AUC', 0.6662964641885766), ('Lead Scoring', -0.008598452278589854)])
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.