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The cause of poor performance in machine learning is either overfitting or underfitting the data. In this post, you will discover the concept of generalization in machine learning and the problems of overfitting and underfitting that go along with it. Read more on this link
Breaking it down: A supervised machine learning algorithm (as opposed to an unsupervised machine learning algorithm) is one that relies on labeled input data to learn a function that produces an appropriate output when given new unlabeled data.Read more on this link
IntroductionK-Nearest Neighbors is the supervised machine learning algorithm used for classification and regression. It manipulates the training data and classifies the new test data based on distance metrics. It finds the k-nearest neighbors to the test data, and then classification is performed by the majority of class labels. Read more on the link
IntroductionLet us talk about the weather. It rains only if it’s a little humid and does not rain if it’s windy, hot or freezing. In this case, how would you train a predictive model and ensure that there are no errors in forecasting the weather? You may say that there are many learning algorithms to choose from. They are distinct in many ways but there is a major difference in what we expect and what the model predicts. That’s the concept of Bias and Variance Tradeoff. Read more on the link
What is K-Fold Cross Validation?K-fold cross-validation is a technique for evaluating predictive models. The dataset is divided into k subsets or folds. The model is trained and evaluated k times, using a different fold as the validation set each time. Performance metrics from each fold are averaged to estimate the model’s generalization performance. This method aids in model assessment, selection, and hyperparameter tuning, providing a more reliable measure of a model’s effectiveness.Read more on the link
What is Principal Component Analysis?The Principal Component Analysis is a popular unsupervised learning technique for reducing the dimensionality of data. It increases interpretability yet, at the same time, it minimizes information loss. It helps to find the most significant features in a dataset and makes the data easy for plotting in 2D and 3D. Read more on the link
Q3. What is the difference between ridge regression and lasso bias?A. Ridge Regression adds a penalty term proportional to the square of the coefficients, while Lasso adds a penalty term proportional to the absolute value of the coefficients, which can lead to variable selection...Read more on the link
What Is Feature Selection in Machine Learning?The goal of feature selection techniques in machine learning is to find the best set of features that allows one to build optimized models of studied phenomena. Read more on the link
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