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How to Build a Labelled Training Data Set with a Few Labelled Examples

When building a classifier, we assume a large enough training data set with labels is available. This situation is what we call as supervised learning. In a real world setting, such training examples with labels need to be acquired. In any application domain where labeling requires domain expertise such as in medicine, gathering a large training set with labels is an expensive and time consuming…

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#label propagation #semi-supervised classification #semi-supervised clustering #Semi-supervised learning

How to Perform Regression with more Predictors than Observations

A common scenario in multiple linear regression is to have a large set of observations/examples wherein each example consists of a set of measurements made on a few independent variables, known as predictors, and the corresponding numeric value of the dependent variable, known as the response. These examples are then used to build a regression model of the following form:

The equation states…

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#dimensionality reduction #multicollinearity #multiple linear regression #multivariate regression #OLS #ordinary least squares regression #Partial Least Squares #PCA #PCR #pls #plsdepot #ridge regression #shrinkage methods #supervised learning #unsupervised learning

Dimensionality Reduction via Linear Discriminant Analysis

In my previous post, I had written about principal component analysis (PCA) for dimensionality reduction. In PCA, the class label of each and every example, even if available, is ignored and only the feature values from each example are considered. Thus, PCA is considered an unsupervisedapproach. The emphasis in PCA is to preserve data variability as much as possible while reducing the…

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#dimensionality reduction #LDA #linear discriminant analysis #multiple discriminant analysis #PCA #Principal component analysis #unsupervised learning

Principal Component Analysis (PCA) Explained with Examples

Any machine learning model building task begins with a collection of data vectors wherein each vector consists of a fixed number of components. These components represent the measurements, known as attributes or features, deemed useful for the given machine learning task at hand. The number of components, i.e. the size of the vector, is termed as the dimensionality of the feature space. When the…

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#Correlation #Covariance #dimensionality reduction #linear transformation #PCA #Scikit-learn #singular value decomposition #small sample correction #unsupervised learning

Machine Learning 2 Deep Learning Recently, I had the pleasure of giving a talk on machine learning and deep learning. I thought my blog readers may like the slides from this talk.

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