Hema AnushaBootstrapped Aggregation(Bagging):Bootstrap Aggregating, also knows as bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy…4 min read·Jan 28, 2022--1--1
Hema AnushaEnsemble Models:Ensemble methods are techniques that aim at improving the accuracy of results in models by combining multiple models instead of using a…1 min read·Jan 26, 2022----
Hema AnushaDECISION TREE IN MACHINE LEARNING:Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is…8 min read·Jan 25, 2022----
Hema AnushaSupport Vector Machine(SVM):Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as…13 min read·Jan 19, 2022----
Hema AnushaLinear Regression in Machine Learning:Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc.5 min read·Jan 11, 2022----
Hema AnushaHyperparameter tuning: Grid search and Random search:Hyperparameters are model-specific properties that are ‘fixed’ even before the model is trained or tested on the data. For Example: In the…3 min read·Dec 29, 2021----
Hema AnushaLoss Minimization Interpretation of Logistic RegressionBinary classification involves 0/1 loss(non-convex) and when data is not perfectly separable then we like to minimize number of error or…3 min read·Dec 28, 2021----
Hema AnushaLogistic Regression in Machine Learning:Logistic regression is one of the most popular Machine Learning algorithms, which comes under the Supervised Learning technique. It is used…8 min read·Dec 26, 2021----
Hema AnushaNaive Bayes in Machine Learning:Naive Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems. It…9 min read·Dec 22, 2021----
Hema AnushaPerformance metrics in Machine Learning:Performance Metrics for Classification Problems:8 min read·Dec 19, 2021----