Precision and recall

Both precision and recall are related to the four outcomes discussed in the prior lesson and are important evaluation metrics for any machine learning model. An ad CTR model should ideally have high precision (high ROI on ad spend) and recall (relevant audience targeting). Although it is possible to calculate precision and recall by hand, sklearn has some handy implementations that you can easily plug into the existing workflow. In this exercise, you will set up a decision tree and calculate precision and recall.

The pandas module is available as pd in your workspace and the sample DataFrame is loaded as df. The features are loaded in X and the target is loaded in y for use. Additionally, precision_score() and recall_score() from sklearn.metrics are available.

This exercise is part of the course

Predicting CTR with Machine Learning in Python

Exercise instructions

Obtain the training and testing splits for X and y.
Define a decision tree classifier and produce predictions y_pred by fitting the model.
Use implementations from sklearn to get the precision and recall scores.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Set up training and testing split
X_train, X_test, y_train, y_test = ____(
  ____, ____, test_size = .2, random_state = 0)

# Create classifier and make predictions
clf = ____
y_pred = clf.____(____, _____).____(X_test) 

# Evaluate precision and recall
prec = ____(y_test, ____, average = 'weighted')
recall = ____(y_test, ____, average = 'weighted')
print("Precision: %s, Recall: %s" %(prec, recall))

Edit and Run Code

This exercise is part of the course

Predicting CTR with Machine Learning in Python

IntermediateSkill Level

5.0+

Start Course for Free

Chances are you’re on this page because you clicked a link. In this chapter, you’ll learn why click-through-rates (CTR) are integral to targeted advertising, how to perform basic DataFrame manipulation, and how you can use machine learning models to predict CTR.

Exercise 1: Introduction to click-through rates Exercise 2: Beginning steps Exercise 3: Feature exploration Exercise 4: First evaluation of data Exercise 5: Overview of machine learning models Exercise 6: Logistic regression for breast cancer Exercise 7: Logistic regression for images Exercise 8: A second toy model Exercise 9: CTR prediction using decision trees Exercise 10: Model implementation Exercise 11: A first CTR model Exercise 12: Beyond only accuracy

This chapter provides the foundations for exploratory data analysis (EDA). Using sample data you’ll use the pandas library to look at columns and data types, explore missing data, and use hashing to perform feature engineering on categorical features. All of which are important when exploring features for more accurate CTR prediction.

Exercise 1: Exploratory data analysis Exercise 2: A first look Exercise 3: Checking for missing values Exercise 4: Distributions by CTR Exercise 5: Feature engineering Exercise 6: Analyzing datetime columns Exercise 7: Converting categorical variables Exercise 8: Creating new features Exercise 9: Standardizing features Exercise 10: Log normalization Exercise 11: Understanding standardization Exercise 12: Standard scaling

It’s time to dive deeper. Find out how you can use measures of model performance including precision and recall to answer real-world questions, such as evaluating ROI on ad spend. You’ll also learn ways to improve upon those evaluation metrics, such as ensemble methods and hyperparameter tuning.

Exercise 1: Applications of metric evaluation Exercise 2: Four categories of outcomes Exercise 3: Evaluating four categories Exercise 4: ROI on ad spend Exercise 5: Model evaluation Exercise 6: Precision and recall

Current Exercise

Exercise 7: Baseline Exercise 8: Classifier comparison Exercise 9: Tuning models Exercise 10: Regularization Exercise 11: Cross validation Exercise 12: Model selection Exercise 13: Ensembles and hyperparameter tuning Exercise 14: Understanding hyperparameter tuning Exercise 15: Random forests Exercise 16: Grid search

Profits can be heavily impacted by your campaign’s CTR. In this chapter, you’ll learn how deep learning can be used to reduce that risk. You’ll focus on multi-layer perceptron (MLP) and neural network models, and learn how these can be used to capture the complex relationship between variables to more accurately predict CTR. Lastly, you’ll explore how to apply the basics of hyperparameter tuning and regularization to classification models.

Exercise 1: Introduction to deep learning Exercise 2: Understanding MLPs Exercise 3: Beginning model Exercise 4: MLPs for CTR Exercise 5: Hyperparameter tuning in deep learning Exercise 6: Hyperparameter tuning in MLPs Exercise 7: Varying hyperparameters Exercise 8: MLP Grid Search Exercise 9: Model evaluation Exercise 10: F-beta score Exercise 11: Low precision and high AUC Exercise 12: Precision, ROI, and AUC Exercise 13: Model review and comparison Exercise 14: Model comparison warmup Exercise 15: Evaluating precision and ROI Exercise 16: Total scoring Exercise 17: Wrap-up video