With the availability of high performance CPUs and GPUs, it is pretty much possible to solve every regression, classification, clustering and other related problems using machine learning and deep learning models. However, there are still various factors that cause performance bottlenecks while developing such models. Large number of features in the dataset is one of the factors that affect both the training time as well as accuracy of machine learning models. You have different options to deal with huge number […]

In our previous article Implementing PCA in Python with Scikit-Learn, we studied how we can reduce dimensionality of the feature set using PCA. In this article we will study another very important dimensionality reduction technique: linear discriminant analysis (or LDA). But first let’s briefly discuss how PCA and LDA differ from each other. PCA vs LDA: What’s the Difference? Both PCA and LDA are linear transformation techniques. However, PCA is an unsupervised while LDA is a supervised dimensionality reduction technique. […]

Random forest is a type of supervised machine learning algorithm based on ensemble learning. Ensemble learning is a type of learning where you join different types of algorithms or same algorithm multiple times to form a more powerful prediction model. The random forest algorithm combines multiple algorithm of the same type i.e. multiple decision trees, resulting in a forest of trees, hence the name “Random Forest”. The random forest algorithm can be used for both regression and classification tasks. How […]

When studying Probability & Statistics, one of the first and most important theorems students learn is the Bayes’ Theorem. This theorem is the foundation of deductive reasoning, which focuses on determining the probability of an event occurring based on prior knowledge of conditions that might be related to the event. The Naive Bayes Classifier brings the power of this theorem to Machine Learning, building a very simple yet powerful classifier. In this article, we will see an overview on how […]

Hierarchical clustering is a type of unsupervised machine learning algorithm used to cluster unlabeled data points. Like K-means clustering, hierarchical clustering also groups together the data points with similar characteristics. In some cases the result of hierarchical and K-Means clustering can be similar. Before implementing hierarchical clustering using Scikit-Learn, let’s first understand the theory behind hierarchical clustering. Theory of Hierarchical Clustering There are two types of hierarchical clustering: Agglomerative and Divisive. In the former, data points are clustered using a […]

Introduction A typical machine learning process involves training different models on the dataset and selecting the one with best performance. However, evaluating the performance of algorithm is not always a straight forward task. There are several factors that can help you determine which algorithm performance best. One such factor is the performance on cross validation set and another other factor is the choice of parameters for an algorithm. In this article we will explore these two factors in detail. We […]