Ta-Seen

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, ...

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 ...