Author: Ashutosh Tripathi

What is the Significance of ROC AUC Curve?

ROC AUC curve helps you to determine the threshold of binary classification problems in machine learning. In Machine Learning classification problems are based on the probability value and its not always correct to have the threshold as 0.5. It depends on the type and domain of the problem. For example in a legal case you don’t want the false positive to be high or it should be at least as possible. so the threshold in this case would be very high. the term AUC that is Area under curve tells us the model goodness of fit. It is used to do the comparative analysis between different classifiers and identify which one is performing good.

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What is Feature Scaling in Machine Learning | Normalization vs Standardization

Let me start with simple question. Can we compare Mango and Apple? Both have different features in terms of tastes, sweetness, health benefits etc. So comparison can be performed between similar entities else it will be biased. Same logic applies to Machine Learning as well. Feature Scaling in Machine Learning brings features to the same scale before we apply any comparison or model building. Normalization and Standardization are the two frequently used techniques of Feature Scaling in Machine Learning.

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How to deploy Machine Learning Models using Flask

In this post, we will build an application using flask which will predict the house price based on the parameters influencing the house price. Later you can customize it for any model GUI designing. We will be using Flask which is widely used web framework for Machine Learning model deployment. There are other frameworks as well coming in the market like FastAPI but till today, flask is still the widely used and trusted framework over the machine learning community for model deployment.

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