Learning with Imbalanced Data: Cost-Sensitive Learning

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Questions and Answers

What is the primary objective of cost-sensitive learning?

To reduce the number of features in the training dataset

To balance the class distribution in the training dataset

To increase the accuracy of the model regardless of cost

To minimize the cost of a model on a training dataset (correct)

What is the relationship between cost-sensitive learning and learning from imbalanced datasets?

Cost-sensitive learning is a subset of learning from imbalanced datasets

They are the same and interchangeable terms

There is considerable overlap between the two, but they are not the same (correct)

They are mutually exclusive and have no overlap

According to Peter Turney, how many types of costs are there in Machine Learning?

Seven

Nine (correct)

Eleven

Five

What is the focus of this course in terms of cost in Machine Learning?

Only one type of cost in imbalanced learning Signup and view all the answers

What is the term used to describe the process of training a model that takes into account different costs, such as the cost of predictive error?

Cost-sensitive learning Signup and view all the answers

What does the specificity metric represent in imbalanced classification?

1 - False Positive Rate Signup and view all the answers

What is the formula to calculate Youden's J statistic?

J = Sensitivity + Specificity - 1 Signup and view all the answers

What is the best way to find the optimal threshold for a binary classification model?

By maximizing Youden's J statistic Signup and view all the answers

What is the purpose of the PR curve in imbalanced classification?

To plot the precision and recall at different thresholds Signup and view all the answers

What is the characteristics of a model with perfect skill in the PR curve?

A point at (1,1) Signup and view all the answers

What is the significance of the G-mean in imbalanced classification?

It is an unbiased evaluation metric Signup and view all the answers

What is the relationship between the size of the error gradient and the correction needed during training?

A small error gradient requires a small correction Signup and view all the answers

What is the purpose of the hyperparameter scale_pos_weight in XGBoost?

To scale the gradient for the positive class Signup and view all the answers

What is the effect of setting scale_pos_weight to 100 for an imbalance of 1:100?

Errors on the minority class are given 100 times more weight Signup and view all the answers

What is the risk of overcorrecting the errors on the positive class?

The model will overfit the minority class Signup and view all the answers

Why is the scale_pos_weight hyperparameter necessary for imbalanced classification problems?

To increase the importance of the minority class Signup and view all the answers

What is the relationship between the correction made during training and the error gradient?

A large error gradient results in a large correction Signup and view all the answers

What is the primary difference between Random Forest and bagging?

Random Forest uses a small randomly selected subset of features for decision trees. Signup and view all the answers

What is the purpose of fitting a subsequent tree on the weighted dataset?

To correct the errors from the previous decision tree Signup and view all the answers

What is the purpose of modifying the purity calculation algorithm in Decision Tree for imbalanced data?

To favor the minority class and tolerate false positives for the majority class. Signup and view all the answers

What does the `class_weight` argument in SkLearn's `RandomForestClassifier` do?

It picks up the inverse ratio from the training data. Signup and view all the answers

What is the main difference between anomaly detection and one class classification?

Anomaly detection is about detecting outliers Signup and view all the answers

What is the downside of using One Class Classification (OCC) for imbalanced classification?

The positive samples are not used in training Signup and view all the answers

What is the main difference between `RandomForestClassifier` and `BalancedRandomForestClassifier`?

One provides random undersampling. Signup and view all the answers

What is the characteristic of outliers in imbalanced datasets?

They are rare compared to majority inliers Signup and view all the answers

How does AdaBoost work?

It uses a sequence of boosted decision trees. Signup and view all the answers

What is the goal of one class classification?

To classify new samples as normal or outliers Signup and view all the answers

What is the purpose of the EasyEnsembleClassifier?

To select all examples from the minority class and a subset from the majority class. Signup and view all the answers

What is the purpose of using one class classification in imbalanced datasets?

To detect the anomaly when the positive class is too infrequent Signup and view all the answers

Study Notes

Cost-Sensitive Learning

Cost-sensitive learning is a sub-field of Machine Learning that accounts for different costs (e.g., cost of predictive error) into training the model.
Goal of cost-sensitive learning is to minimize the cost of a model on a training dataset.
Cost-sensitive learning and learning from imbalanced dataset are not the same but have considerable overlap.

Costs in Machine Learning

According to Peter Turney, there are nine types of costs in ML, but we are dealing with only one in imbalanced learning.
Specificity is 1- FPR, making it an unbiased evaluation metric for imbalanced classification.

Youden's J Statistic

Youden's J statistic is used to optimize the threshold for classification.
J = Sensitivity + Specificity - 1
This is the threshold corresponding to argmax(tpr-fpr).

Moving Probability Threshold using PR Curve

PR curve is a plot of precision and recall at different thresholds.
Precision is Positive Predictive Power (True Positives / (True Positives + False Positives)).
Recall or sensitivity is True Positives / (True Positives + False Negatives).
A model with perfect skill is depicted as a point at (1,1).

Weighted XGBoost for Imbalanced Data

The scale_pos_weight value is used to scale the gradient for the positive class.
This scales the model's errors made during training on the positive class and encourages the model to overcorrect them.
For an imbalance of 1:100, this can be set to 100.

Weighted Random Forest for Imbalanced Classification

Random Forest is similar to bagging but has a slight difference (bootstrap samples).
Decision Tree typically uses a modified purity calculation algorithm to reflect class weighting.
This favors a mixture that favors the minority class and tolerates false positives for the majority class.

Weighted Random Forest with SkLearn and ImbLearn

SkLearn RandomForestClassifier with class-Weight class_weight argument takes a dictionary for 0 and 1 labels.
If balanced, it picks up the inverse ratio from training data.
With bootstrap class_weight, class weight is set at the bootstrap sample level.
Imblearn BalancedRandomForestClassifier provides random undersampling.

Ensemble with Adaboost

Imbalanced_learn library provides EasyEnsembleClassifier.
Select all examples from the minority class and the subset from the majority class.
AdaBoost is a sequence of boosted decision trees.
It works by first fitting a decision tree on the dataset, then determining the errors made by the tree and weighing the dataset's examples by those errors.

One Class Classifier and Overall Steps

One class classifier is an ML approach to detect anomalies.
These algorithms are trained on majority inliers or normal data.
The trained models are used to classify new samples as outlier (positive) or normal (negative).

Downside of using OCC for Imbalanced Classification

The positive samples (however small in number) are NOT used in training at all.
The advantage of this technique comes at a price.

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Description

This quiz covers the concept of cost-sensitive learning in machine learning, a sub-field that accounts for different costs in predicting incorrect outcomes. It explores the techniques used in cost-sensitive decision trees and their applications in imbalanced datasets. Test your knowledge of this important concept in AI and ML.