Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

Machine Learning
  • 12 Videos | 1h 28m 4s
  • Includes Assessment
  • Earns a Badge
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The imbalanced-learn library that integrates with Pandas ML (machine learning) offers several techniques to address the imbalance in datasets used for classification. In this course, explore oversampling, undersampling, and a combination of techniques. Begin by using Pandas ML to explore a data set in which samples are not evenly distributed across target classes. Then apply the technique of oversampling with the RandomOverSampler class in the imbalanced-learn library; build a classification model with oversampled data; and evaluate its performance. Next, learn how to create a balanced data set with the Synthetic Minority Oversampling Technique and how to perform undersampling operations on a data set by applying Near Miss, Cluster Centroids, and Neighborhood cleaning rules techniques. Next, look at ensemble classifiers for imbalanced data, applying combination samplers for imbalanced data, and finding correlations in a data set. Learn how to build a multilabel classification model, explore the use of principal component analysis, or PCA, and how to combine use of oversampling and PCA in building a classification model. The exercise involves working with imbalanced data sets.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 18s
    UP NEXT
  • Playable
    2. 
    Analyzing an Imbalanced Dataset
    7m 36s
  • Locked
    3. 
    The RandomOverSampler
    8m 35s
  • Locked
    4. 
    The SMOTE Oversampler
    4m 14s
  • Locked
    5. 
    Undersampling Using imbalanced-learn
    8m 51s
  • Locked
    6. 
    Ensemble Classifiers for Imbalanced Data
    6m 15s
  • Locked
    7. 
    Combination Samplers
    7m 22s
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    8. 
    Finding Correlations in a Dataset
    8m 18s
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    9. 
    Building a Multi-Label Classification Model
    3m 22s
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    10. 
    Dimensionality Reduction with PCA
    9m 41s
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    11. 
    Imbalanced Learn and PCA
    8m 57s
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    12. 
    Exercise: Working with Imbalanced Datasets
    7m 34s

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