Posts

Understanding Confusion Matrix by CHIRAG

Image
What Is a Confusion Matrix? A confusion matrix is a performance evaluation tool in machine learning, representing the accuracy of a classification model.  It displays the number of true positives, true negatives, false positives, and false negatives. A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the total number of target classes.     For a binary classification problem, we would have a 2 x 2 matrix, as shown below, with 4 values: Let’s understanding this matrix: The target variable has two values: 0 & 1 The  columns  represent the  Predicted values  of the target variable The  rows  represent the  Actual values  of the target variable. But wait – what’s TP, FP, FN, and TN here? That’s the crucial part of a confusion matrix. Let’s understand each term below. Important Terms in a Confusion Matrix True Positive (TP)  The predicted value matches the actual value...
Post a Comment
Read more

Linear Regression Understanding by CHIRAG

Image
  Linear Regression 1 .)Linear regression is a supervised learning algorithm used when target / dependent variable continues real number. 2.)Linear regression is a type of statistical analysis used to predict the relationship between dependent and independent Variables using BEST FIT LINE. 3.) It work on the principle of ordinary least square (OLS)/ Mean square errror (MSE) Simple Linear Regression In a simple linear regression, there is one independent variable and one dependent variable. The model estimates the slope and intercept of the line of best fit, which represents the relationship between the variables. The slope represents the change in the dependent variable for each unit change in the independent variable, while the intercept represents the predicted value of the dependent variable when the independent variable is zero. Mathematically, we can represent a linear regression as: y= a0+a1x+ ε Y= Dependent Variable (Target Variable) X= Independent Variable (predictor Variab...
Post a Comment
Read more

SVMs Classification Understanding by CHIRAG

Image
What is a Support Vector Machine?  1.)  It is a supervised machine learning problem where we try to find a hyperplane that best separates the two classes.   2.) Support Vector Machines   (SVMs in short) are machine learning algorithms that are used for classification and regression purposes. SVMs are one of the powerful machine learning algorithms for classification, regression and outlier detection purposes.  3.)  An SVM classifier builds a model that assigns new data points to one of the given categories.   4.)Thus, it can be viewed as a non-probabilistic binary linear classifier.   Don’t get confused between SVM and logistic regression. Both the algorithms try to find the best hyperplane, but the main difference is logistic regression is a probabilistic approach whereas support vector machine is based on statistical approaches. Support Vector Machines intuition   Now, we should be familiar with some SVM terminology. Hyperplane A hyperplane...
Post a Comment
Read more