KNN Algorithm (Classification) in Machine Learning
KNN Algorithm -When is KNN Algorithm Suitable? KNN Algorithm-Assumptions of KNN:1.k-NN performs much better when all of the data are the same scale.2.k-NN performs well with a limited number of input variables , and it ...
Read moreLogistic Regression in Machine learning
When is Logistic regression Suitable? 1. If your data is binary> 0/1, Yes/No, True/False.2. If you need probabilistic results3. when you need a linear decision boundary4. If you need to understand the impact of a ...
Read moreGenerative AI – What is it and How Does it Work?
Generative AI allows users to easily create new content from a variety of inputs. These models can accept and output text, images, audio,video, animations, 3D models, and other types of data. How Does Generative AI Work? ...
Read moreModel Retraining Retrain Machine learning Models
Model retraining in machine learning (ML) is the process of updating an existing model to maintain or improve its performance over time as new data becomes available. It is essential because models often degrade in ...
Read moreCICD Pipeline – 4 Stages of Deployment in ML
CICD Pipeline -CI denotes Continuous Integration, and CD stands for Continuous Delivery. Continuous integration enables teams to work on code, data, and features at the same time and submit them to a single repository numerous ...
Read more6 Steps towards a Successful Machine Learning Project
Machine learning has transformed industries, not only in how companies operate, but also in how they build solutions. The uses of machine learning are almost limitless, from consumer prediction to disease diagnosis. However, it is ...
Read moreOne Hot Encoding in Machine Learning
One hot encoding is a technique for representing categorical variables as numerical values in a machine learning model. Introduction ML is built upon the power of data, which can turn raw data into useful information. ...
Read moreLasso, Ridge and Elastic Net Regression in ML
Regularization. Regularization is a regression technique that prevents or regulates the estimated coefficient from shrinking to zero. In other words, one does not encourage the formulation of more complex or flexible models to reduce the ...
Read moreMachine Learning-Adusted R-squared and R-squared
Machine Learning-R-squared (R²) It calculates the proportion of the variation in your dependent variable that can be explained by all of the independent variables in the model. It is assumed that each independent variable in ...
Read more2 Issues-Degrades Machine learning Model performance
The 2 Main Problems in Machine learning Overfitting and underfitting are the two most typical machine learning problems that affect model performance. Before we get into overfitting and underfitting, let’s establish some crucial concepts that ...
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