TowardsMachineLearning

Machine Learning

What are Boosting Algorithms and how they work

Boosting Algorithms In Machine Learning Ensemble Learning and Ensemble Method Ensemble Learning is a method that is used to enhance the performance of Machine Learning model by combining several learners. When compared a single model , this type of learning builds models with improved efficiency and accuracy. Suppose you ask a complex question to thousands …

What are Boosting Algorithms and how they work Read More »

Process to write Research Papers and Journals for Data Scientists

What is a Research Paper? A research paper is an expanded essay that presents your own interpretation or evaluation or argument. When you write an essay, you use everything that you personally know and have thought about a subject. When you write a research paper you build upon what you know about the subject and make a …

Process to write Research Papers and Journals for Data Scientists Read More »

What is K-Means algorithm and how it works

1.  Overview K-means clustering is a simple and elegant approach for partitioning a data set into K distinct, nonoverlapping clusters. To perform K-means clustering, we must first specify the desired number of clusters K; then, the K-means algorithm will assign each observation to exactly one of the K clusters. The below figure shows the results …

What is K-Means algorithm and how it works Read More »

Random Forest

What is Random Forest and how it works

1. Overview Random forest is a machine learning approach that utilizes many individual decision trees. In the tree-building process, the optimal split for each node is identified from a set of randomly chosen candidate variables. Besides their application to predict the outcome in classification and regression analyses, Random Forest can also be applied to select …

What is Random Forest and how it works Read More »

decision tree thumbnail

Decision Tree Algorithm overview explained

1. Overview Decision Tree Analysis is a general, predictive modelling tool with applications spanning several different areas. In general, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on various conditions. It is one of the most widely used and practical methods for supervised learning. Decision Trees …

Decision Tree Algorithm overview explained Read More »

Naïve Bayes Algorithm overview explained

1. Overview Naive Bayes is a very simple algorithm based on conditional probability and counting. Essentially, your model is a probability table that gets updated through your training data. To predict a new observation, you’d simply “lookup” the class probabilities in your “probability table” based on its feature values. It’s called “naive” because its core assumption of …

Naïve Bayes Algorithm overview explained Read More »