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Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

The imbalanced-learn library that integrates with Pandas ML offers several techniques to address the imbalance in datasets used for classification. Explore oversampling, undersampling, and a combination of these techniques.



Expected Duration (hours)
1.4

Lesson Objectives

Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

  • use Pandas ML to explore a dataset where the samples are not evenly distributed across the target classes
  • apply the technique of oversampling using the RandomOverSampler class in the imbalanced-learn library, build a classification model with the oversampled data, and evaluate its performance
  • create a balanced dataset using the Synthetic Minority Oversampling Technique and build and evaluate a classification model with that data
  • perform undersampling operations on a dataset by applying the Near Miss, Cluster Centroids, and Neighborhood Cleaning Rule techniques
  • use the EasyEnsembleClassifier and BalancedRandomForestClassifier available in the imbalanced-learn library to build classification models with imbalanced data
  • apply a combination of oversampling and undersampling using the SMOTETomek and SMOTEENN techniques
  • use Pandas and Seaborn to visualize the correlated fields in a dataset
  • train and evaluate a classification model to predict the quality ratings of red wines
  • transform a dataset containing multiple features to a handful of principal components and build a classification model using the reduced dimensions of the dataset
  • combine the use of oversampling and PCA in building a classification model
  • recall the techniques used by algorithms for undersampling and oversampling data and the use of combined samplers
  • Course Number:
    it_dsmdladj_04_enus

    Expertise Level
    Intermediate