Introduction Cross Validation (CV) is a way of training machine learning models in which multiple models are trained on a part of the data and then accessing their performance or testing them on a independent unseen set of data. In the Cross-validation technique, we generally split the original train data ... Read More

Introduction Normality is defined as the phenomenon of belonging to a normal or Gaussian distribution in statistical terms. The normality of a dataset is the test for a dataset or variable if it follows a normal distribution. Many tests can be performed to check the normality of a dataset among ... Read More

Introduction OOB or Out of Bag error and OOB Score is a term related to Random Forests. Random Forest is an ensemble of decision trees that improves the prediction from that of a single decision tree.OOB error is used to measure the error in the prediction of tree-based models like ... Read More

Introduction Today Machine Learning plays a crucial role in predicting stock prices and the growth of popular organizations and investment banks. While working on many such problems we consider many relations and correlations between different kinds of factors. The Anne Hathaway Effect is one such peculiar correlation related to ... Read More

Introduction Similarity metrics are crucial in Recommendation Systems to find users with similar behavior, pattern, or taste. Nowadays Recommendation systems are found in lots of useful applications such as Movie Recommendations as in Netflix, Product Recommendations as in Ecommerce, Amazon, etc. Organizations use preference matrices to capture use behavioral and ... Read More

Introduction Fixed basis functions are functions that help us to extend linear models in Machine Learning, by taking linear combinations of nonlinear functions. Since Linear models depend on the linear combination of parameters, they suffer a significant limitation. The radial function thus helps model such a group of models by ... Read More

Introduction Natural Language Processing for finding the semantic similarity between sentences, words, or text is very common in modern use cases. There are numerous ways to calculate the similarity between texts. One such popular method is cosine similarity. It is used to find the similarity between two vectors that are ... Read More

Introduction In Recommendation Systems, Collaborative filtering is one of the approaches to building a model and finding seminaries between users. This concept is highly used in Ecommerce sites and OTT and video-sharing platforms. One of the highly talked about issues that such systems face while in the initial modeling phase ... Read More

Introduction In Machine Learning, a good model is generated if we have a good representation and amount of data. Data may be divided into different sets that serve a different purposes while training a model. Two very useful and common sets of data are the training and testing set. The ... Read More

Introduction DBSCAN is the abbreviation for Density-Based Spatial Clustering of Applications with Noise. It is an unsupervised clustering algorithm.DBSCAN clustering can work with clusters of any size from huge amounts of data and can work with datasets containing a significant amount of noise. It is basically based on the criteria of a minimum number ... Read More