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.
Data Analyst job is to explore the data, interpret it and finally convert it to information. This information will then help decision makers to take business related decisions. As a Data Analyst your roles and responsibilities includes starting from data collection, interpretation, visualization, dashboard building and finally story telling around the information derived from data.
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.
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.
In this video post I have explained about the below ML FAQ:
1. What is Gini
2. What is Gini Index
3. How to calculate Gini and Gini Index
4. How Gini Index helps to decide the Parent and Decision nodes in Decision Tree
In this video I will be explaining about the following ML questions:
1. How to Interpret Decision Tree
2. How splitting happens in the decision tree.
3. What is Entropy?
4. What in Information Gain?
5. How Information gain helps to decide the parent node and further node split?