Machine Learning and Deep Learning

  Top of Form Bottom of Form   Logistic Regression ·          Logistic regression is widely used for binary classification problems. It gives binary output. A binary dependent variable can have only two values, like 0 or 1, win or lose, pass or fail, healthy or sick, etc. ·          Logistic Regression: Output is in Binary ·          It can also be extended to multi-class classification problems whose output can be classified into more than one category. ·          Logistic regression can be used to predict a dependent variable(output) on the basis of continuous and/or categorical independents(input) ·          The dependent variable(output) is categorical: y ϵ {0, 1} ·          It is scalable...

  Performance Metrics for the Regression model In a machine learning Algorithm, once the model is built, the next step is to use of various performance criteria to evaluate Machine learning Models. Various performance Metrics/Criteria used for regression models and Classification models are different and are listed below. Regression Model:  In Regression analysis output is a continuous value, therefore for Regression performance following methods are used Mean squared error (MSE) Mean average error(MAE) Root mean squared error (RMSE) R Square Classification Model:   In the classification model output is Discret form and for classification performance following methods are used Confusion matrix Accuracy Precision Recall (sensitivity) Specificity  ROC curve (AUC) ROC Area Under Curve is useful when we are not concerned about whether the small dataset/class of dataset is positive or not, in contrast to the F1 score wh...

Linear Regression and Gradient Decent method to learn model of Linear Regression.   Linear Regression Linear regression: Machine Learning Algorithm  Variables in Linear regression Independent variable Ø  If X is input numerical variable then X is called the independent variable or predictor.It is input of the model. All Features /Co-variant are independent variable   Dependent Variable Ø  If Y is output numerical variable. Y is also called the dependent variable or response variable. It Output of a model Machine learning models are built to derive the relationship between the dependent variable and independent variable.  It predicts a continuous dependent variable based on values of the independent variable in case of  Linear Regression Ø  Linear regression is Supervised Learning. It predicts Relationship between dependent and an independent variable which is linear Ø  e.g Income *  Expendit...