Welcome to this Blog series on Neural Networks. In the series 204.5 we will go from basics of neural networks to build a neural network model that recognizes digit images and reads them correctly. In this post we will just revise our understanding of how logistic regression works, which can …

Read More »## 204.5.2 Decision Boundary – Logistic Regression

In last session we recapped logistic regression. There is something more to understand before we move further which is Decision Boundary. Once we get decision boundary right we can move further to Neural networks. Decision Boundary – Logistic Regression The line or margin that separates the classes Classification algorithms are …

Read More »## 204.2.6 Model Selection : Logistic Regression

We left some part of the post regarding goodness of fitness behind. We will cover them in this post and see if we can improve our model based on AIC and BIC. We will also cover various methods used for model selection in a series dedicated to it. How to …

Read More »## 204.2.5 Multicollinearity and Individual Impact Of Variables in Logistic Regression

Previous post was about goodness of fit, we covered Confusion matrix and will cover the rest in next posts too. But first let’s deal with a common issue with modeling: Multicollinearity The relation between X and Y is non linear, we used logistic regression The multicollinearity is an issue related …

Read More »## 204.2.4 Goodness of fit for Logistic Regression

Goodness of Fit for a Logistic Regression There are quite a lot methods to find how good a model is. However, we will stick to most important measures applicable for a logistic regression model. Classification Matrix AIC and BIC ROC & AUC Out of these three we will go through …

Read More »## 204.2.2 Logistic Function to Regression

In last post we saw linear regression cannot be used if the final output is binary, yes or no. As it’s tough to fit a binary output on a linear function. To solve this problem we can move toward some different kind of functions, a Logistic Function being the first …

Read More »## 204.2.1 Logistic Regression, why do we need it?

In this series we will try to explore Logistic Regression Models. For the starters we will do a recap of Linear Regression and see if it works all the time. Practice : What is the need of logistic regression? Dataset: Product Sales Data/Product_sales.csv What are the variables in the dataset? …

Read More »## 203.5.5 Practice : Implementing Intermediate outputs in R

In this post we will learn how to implement the concept of intermediate outputs using R. We will cover many things in this session. Dataset: Emp_Productivity/ Emp_Productivity_All_Sites.csv Filter the data and take first 74 observations from above dataset . Filter condition is Sample_Set<3 Build a logistic regression model to predict …

Read More »## 203.5.4 Issue with Non Linear Decision Boundary

In previous post we just tried solving a non linear data using linear boundary. We can use multiple linear boundaries to separate classes in this kind of datasets. However, this will cause some issues which we will understand bu visualizing the problem and think about a possible solution too. Non-Linear …

Read More »## 203.5.3 Practice : Non Linear Decision Boundary

Linear decision boundaries is not always way to go, as our data can have polynomial boundary too. In this post we will just see what happens if we try to use a linear function to classify a bit complex data. LAB: Non-Linear Decision Boundaries Dataset: “Emp_Productivity/ Emp_Productivity.csv” Draw a scatter …

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