Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML

Machine Learning    |    Intermediate
  • 12 videos | 1h 23m 4s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 8 users Rating 5.0 of 8 users (8)
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

  • 2m 18s
  • 7m 36s
    Learn how to use Pandas ML to explore a dataset where the samples are not evenly distributed across the target classes. FREE ACCESS
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    3.  The RandomOverSampler
    8m 35s
    In this video, you will learn how to apply the technique of oversampling using the RandomOverSampler class in the imbalanced-learn library. You will also build a classification model with the oversampled data, and evaluate its performance. FREE ACCESS
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    4.  The SMOTE Oversampler
    4m 14s
    In this video, you will learn how to create a balanced dataset using the Synthetic Minority Oversampling Technique and build and evaluate a classification model with that data. FREE ACCESS
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    5.  Undersampling Using imbalanced-learn
    8m 51s
    Learn how to perform undersampling operations on a dataset by applying the Near Miss, Cluster Centroids, and Neighborhood Cleaning Rule techniques. FREE ACCESS
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    6.  Ensemble Classifiers for Imbalanced Data
    6m 15s
    In this video, learn how to use the EasyEnsembleClassifier and BalancedRandomForestClassifier available in the imbalanced-learn library to build classification models with imbalanced data. FREE ACCESS
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    7.  Combination Samplers
    7m 22s
    In this video, learn how to apply a combination of oversampling and undersampling using the SMOTE and Tomek techniques. FREE ACCESS
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    8.  Finding Correlations in a Dataset
    8m 18s
    In this video, find out how to use Pandas and Seaborn to visualize the correlation between fields in a dataset. FREE ACCESS
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    9.  Building a Multi-Label Classification Model
    3m 22s
    In this video, you will learn how to train and evaluate a classification model to predict the quality ratings of red wines. FREE ACCESS
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    10.  Dimensionality Reduction with PCA
    9m 41s
    In this video, you will transform a dataset containing multiple features to a handful of principal components and build a classification model using the reduced dimensions of the dataset. FREE ACCESS
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    11.  Imbalanced Learn and PCA
    8m 57s
    In this video, you will learn how to use oversampling and PCA together to build a classification model. FREE ACCESS
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    12.  Exercise: Working with Imbalanced Datasets
    7m 34s
    Upon completion of this video, you will be able to recall the techniques used by algorithms for undersampling and oversampling data, as well as the use of combined samplers. FREE ACCESS

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