Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML
Machine Learning
| Intermediate
- 12 videos | 1h 23m 4s
- Includes Assessment
- Earns a Badge
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
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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
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1.Course Overview2m 18sUP NEXT
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2.Analyzing an Imbalanced Dataset7m 36s
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3.The RandomOverSampler8m 35s
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4.The SMOTE Oversampler4m 14s
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5.Undersampling Using imbalanced-learn8m 51s
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6.Ensemble Classifiers for Imbalanced Data6m 15s
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7.Combination Samplers7m 22s
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8.Finding Correlations in a Dataset8m 18s
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9.Building a Multi-Label Classification Model3m 22s
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10.Dimensionality Reduction with PCA9m 41s
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11.Imbalanced Learn and PCA8m 57s
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12.Exercise: Working with Imbalanced Datasets7m 34s
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