Posts

Overfitting Problem in Machine Learning and Regularization in Linear and Logistic Regression

Image
Well, In Researchgate and few other platforms, the topic of the overfitting problem and its reduction process in Linear Regression and Logistic Regression is ubiquitous. To the best of my knowledge, I am trying to write about this topic here... Before writing anything about the reduction process, it is a must to know what is overfitting. Okay, let's come to the point with an example first, If you, unfortunately,   buy jeans pant for you which is larger in size than yours and the shop from where you have bought the pant, they have no changing and returning options. So, finding no other options available, you have to wear that pant and try to fit yourself in that oversized jeans pant. This thing can be considered as Overfitting.  Now I would like to describe Overfitting in Machine Learning, and here it is....... When we train a statistical model with a huge amount of data, then overfitting occurs in the model or, in other words, the model is considered overfitted model. Let's Co...
Post a Comment
Read more

Receiver Operating Characteristic Curve (ROC)

Image
I t's tough to understand the basic concept of the Receiver Operating  Characteristic  (ROC)  curve.  In my previous blog, I have written about the ROC curve i n short that you can find in the given link:  https://taseenresearch.blogspot.com/2021/04/machine-learning-model-evaluation.html. But here I am trying to explain the ROC curve, briefly maintaining a relationship with the Confusion Matrix. Hope it will help to clear your concepts about the ROC curve and how it evaluates the model's performances.  Roc curve is basically the indicator that e valuates the output quality of the classifier algorithms.  In ROC curves, True-Positive (TP) rates are featured on the Y-axis, and False-Positive (FP) rate featured on the X-axis, which indicates that the top left corner of the plot is an "Ideal" point with a  True-Positive(TP) volume of one and  False-Positive(FP) volume of zero, which indicates a better model.  Have a look at this figure, ...
Post a Comment
Read more

Machine Learning Model Evaluation

Image
I want to write something about model evaluation for a long time, but I can not manage my time. Many people find some difficulties in Model evaluation in Machine Learning, and they can not get the main purpose and ways of evaluating the model's performance. However, I tried to write about this from my little knowledge. Hopefully, it will help others. Machine Learning MoDeL EvaLuaTiOn: Making the  judgement about an amount or value of something or the result of an assessment is known as Evaluation. Okay, coming to the main point is the Model Evaluation of Machine Learning (ML).  In Machine Learning, we generally follow few steps, which are given below:👇 1: Taking the input the dataset 2: Cleaning the dataset (clear the null value) 3: Exploratory Data Analysis 4: Image Augmentation (If the dataset is consisting of image data) 5: Building a Machine Learning Model 6: Model Evaluation  7: Deployment Well, we all know if an individual wants to be the best in Dance, there ...
Post a Comment
Read more