Correlation vs Covariance

  1. What is Logistic Regression? Logistic Regression is one of the machine learning algorithms used for solving classification problems. It is used to estimate probability whether an instance belongs to a class or not. If the estimated probability is greater than threshold, then the model predicts that the instance belongs to that class, or else it predicts that it does not belong to the class as shown in fig 1. This makes it a binary classifier. Logistic regression is used where the value of the dependent variable is 0/1, true/false or yes/no. Example 1 Suppose we are interested to know whether a candidate will pass the entrance exam. The result of the candidate depends upon his attendance in the class, teacher-student ratio, knowledge of the teacher and interest of the student in the subject are all independent variables and result is dependent variable. The value of the result will be yes or no. So, it is a binary classification problem. Practical Implementation of Logistic Regres

  1. What is Logistic Regression? Logistic Regression is one of the machine learning algorithms used for solving classification problems. It is used to estimate probability whether an instance belongs to a class or not. If the estimated probability is greater than threshold, then the model predicts that the instance belongs to that class, or else it predicts that it does not belong to the class as shown in fig 1. This makes it a binary classifier. Logistic regression is used where the value of the dependent variable is 0/1, true/false or yes/no. Example 1 Suppose we are interested to know whether a candidate will pass the entrance exam. The result of the candidate depends upon his attendance in the class, teacher-student ratio, knowledge of the teacher and interest of the student in the subject are all independent variables and result is dependent variable. The value of the result will be yes or no. So, it is a binary classification problem. Why Logistic Regression, Not Linear Regress

  Simple Linear Regression   Simple linear regression is used to find out the best relationship between a single input variable (predictor, independent variable, input feature, input parameter) & output variable (predicted, dependent variable, output feature, output parameter) provided that both variables are continuous in nature. This relationship represents how an input variable is related to the output variable and how it is represented by a straight line. After looking at scatter plot we can understand: The direction The strength The linearity The above characteristics are between variable Y and variable X. The above scatter plot shows us that variable Y and variable X possess a strong positive linear relationship. Hence, we can project a straight line which can define the data in the most accurate way possible. If the relationship between variable X and variable Y is strong and linear, then we conclude that particular independent variable X is the effective input variable to

Understanding the Concept of KNN Algorithm Using R   The huge amount of data that we’re generating every day, has led to an increase of the need for advanced Machine Learning Algorithms. One such well-performed algorithm is the K Nearest Neighbour algorithm. In this blog on KNN Algorithm In R, we will understand what is KNN algorithm in Machine Learning and its unique features including the pros and cons, how the KNN algorithm works, an essay example of it, and finally moving to its implementation of KNN using the  R Language. It is quite essential to know Machine Learning basics. Here’s a brief introductory section on what is Machine Learning and its types. Machine learning  is a subset of Artificial Intelligence that provides machines the power to find out automatically and improve from their gained experience without being explicitly programmed. There are mainly three types of Machine Learning discussed briefly below: Supervised Learning: It is that part of Machine Learning in which