Stokastik

Machine Learning, AI and Programming

Understanding Word Vectors and Word2Vec

Quite recently I have been exploring the Word2Vec tool, for representing words in text documents as vectors. I got the initial ideas about word2vec utility from Google's code archive webpage. The idea behind coming up with this kind of utility caught my interest and later I went on to read the following papers by Mikolov et. al. to better understand the algorithm and its implementation. Efficient Estimation of Word Representations […]

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Learning From Unlabelled Data - EM Approach

Accurately labelled data can be a bottleneck in many machine learning problems as they are difficult and expensive to obtain, and even if we obtain some labelled data, the labels might not be 100% accurate. Many startups working in machine learning space resort to crowdsourcing of the labelling task. Inspired by this research paper, I am going to try and use lots of unlabelled data in addition to small amounts of labelled data […]

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Building a Classification Tree from scratch

In this post I am going to demonstrate an implementation of a classification tree from scratch for multi-label classification. Since most of my work involves working with text classification, hence the classification tree that I am going to demonstrate here has been built, keeping text classification problems in mind. The theoretical explanation about various components and modules about classification tree can be found in this paper. This implementation is not ready-made for […]

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Logistic Regression Analysis with Examples using R

In the last post we had seen how to perform a linear regression on a dataset with R. We had also seen how to interpret the outcome of the linear regression model and also analyze the solution using the R-Squared test for goodness of fit of the model, the t-test for significance of each variable in the model, F-statistic for significance of the overall model, Confidence intervals for the variable […]

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Building Gradient Boosted Trees from scratch

Gradient Boosted Trees are tree ensemble algorithms similar to Random Forests, but unlike Random Forests where trees are constructed independently from each other, in the gradient boosting algorithm, the tree in each round is boosted based on the errors from the tree in the previous round. Although there are more differences in how GBT reduces the error (bias + variance) compared to RF. In this post, we would be constructing boosted trees using […]

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Monte Carlo Sampling Techniques

In the last post, we saw how to sample random values from a target probability distribution (both with discrete as well as continuous distributions) using techniques like inverse CDF method, the transformation method and so on. All of the earlier discussed methods falls under the category of Monte Carlo techniques. In this post, we will be discussing some of the other advanced Monte Carlo techniques and their importance in the […]

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Sampling from Probability Distributions

Often we are required to sample random values from a specified distribution. The specified probability distribution could be either discrete or continuous. With discrete sampling, the generated samples can take only discrete states, for example, a coin toss experiment can either be a heads or a tails, a dice can only come up with result from the set {1, 2, 3, 4, 5, 6} and so on. Whereas with continuous sampling, the generated samples […]

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Dimensionality Reduction using Restricted Boltzmann Machines

Restricted Boltzmann Machine is an unsupervised machine learning algorithm that is useful for doing dimensionality reduction, classification (deep neural networks), regression, collaborative filtering (for recommendation engines), topic modeling etc. The functionality of RBM's are somewhat similar to PCA (SVD), PLSA, LDA etc., which transforms features from the input data into a lower dimension space, capturing the dependencies between different features. RBM's has also been used successfully in problems involving missing/unobserved data. For […]

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Classification with Imbalanced Data Sets

In credit card fraud analysis, most datasets are highly skewed since the number of valid transactions far outweighs the number of fraudulent transactions (in most cases, the ratio of valid transactions to fraudulent transactions could be as skewed as 98% to 2%). Without fitting a classification model to the training data if we simply predict any unknown transaction as a valid transaction, we would be correct 98% of the time.

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