OVERVIEW This blog is going to be very interested for the understanding point of view for ridge ,lasso and elastic net regression . Let first understand what is lasso and Ridge and  ElasticNet. Before moving to Ridge, Lasso and Elastic Net Regression we need an basic idea of Overfitting and Underfitting. Let have an brief idea about it. Overfitting and Underfitting    Overfitting  is a condition where bias is low but variance tends to get high and results in fitting too much in a model, whereas in  Underfitting , the variance is low but bias tends to get high and the model becomes too loose or simplified. Also, when we can’t examine anything from the  machine learning algorithm ,  we can say it is an underfitting condition, whereas, when data provides excessive information that we don’t even need and this data is acting as a burden, we say it is an overfitting problem. Graphical representation of Underfitting, Just Fitting, and Overfitting The p...

  Performance Metrics in Machine Learning Evaluating the performance of a Machine learning model is one of the important steps while building an effective ML model.  To evaluate the performance or quality of the model, different metrics are used, and these metrics are known as performance metrics or evaluation metrics.  These performance metrics help us understand how well our model has performed for the given data. In this way, we can improve the model's performance by tuning the hyper-parameters. Each ML model aims to generalize well on unseen/new data, and performance metrics help determine how well the model generalizes on the new dataset. In machine learning, each task or problem is divided into  classification  and  Regression . Not all metrics can be used for all types of problems; hence, it is important to know and understand which metrics should be used. Different evaluation metrics are used for both Regression and Classification tasks. In this top...

  In this blog I will be writing about Logistic Regression, that is, what is Logistic regression, Cost function, Threshold, checking goodness of fit etc. What  is Logistic Regression? The logistic regression statistic modeling technique is used when we have a binary outcome variable. For example: given the parameters, will the student pass or fail? Will it rain or not? etc. So, though we may have continuous or categorical independent variables, we can use the logistic regression modeling technique to predict the outcome when the outcome variable is binary. Let's see how the algorithm differs from linear regression. Linear regression statistical model is used to predict continuous outcome variables, Whereas logistic regression predicts categorical outcome variables. Linear regression model regression  line is highly susceptible to outliers. So, it will not be appropriate for logistic regression. Where to use logistic regression Logistic regression is used to solve classifi...