Hema AnushaBootstrapped Aggregation(Bagging):Bootstrap Aggregating, also knows as bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy…Jan 28, 20221Jan 28, 20221
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…Jan 26, 2022Jan 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…Jan 25, 2022Jan 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…Jan 19, 2022Jan 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.Jan 11, 2022Jan 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…Dec 29, 2021Dec 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…Dec 28, 2021Dec 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…Dec 26, 2021Dec 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…Dec 22, 2021Dec 22, 2021
Hema AnushaPerformance metrics in Machine Learning:Performance Metrics for Classification Problems:Dec 19, 2021Dec 19, 2021