Motrix precision performance5/3/2023 ![]() The rest of the features are the input parameters. The value 1 means the person has arthritis and value 2 means the person does not have arthritis. This column tells us if a person has arthritis or not. The last column ‘havarth3’ is the target variable that we want to predict using the classifier. Output: Index(, dtype='object', length=108) This DataFrame is so big that I cannot show a screenshot here. Here is the dataset for this project: import pandas as pd import numpy as np df = pd.read_csv('arthritis.csv') But before that, we need to select the features. Please feel free to download the dataset from this link: This dataset contains other parameters and we will use those to predict if a person has arthritis or not. So, first, we will develop a model and then work on the performance evaluation metrics one by one. We need a machine learning model on which we will try all our performance evaluation metrics. I will not dive too deep into them as this is an overview or a cheat sheet. In this article, I will try to explain the performance evaluation metrics for classification models briefly with formulas, simple explanation, and their calculations using a practical example. So, I decided to write this article that summarizes all the popular performance evaluation metrics for classification models. You have to choose the parameters on which you want to evaluate the performance of your model. But in other tools like sklearn or R packages, performance evaluation parameters do not come automatically with the model. Softwares like Weka provides a lot of performance evaluation parameters automatically as you build the model. How do you evaluate the performance of that machine learning model? Then performance evaluation can be a challenge. Anyone can develop machine learning without knowing much about what is going on behind the scene. Because machine learning itself has become pretty easy because of all the libraries and packages. Performance evaluation is the most important part of machine learning in my opinion. Development of a Classification Model and Calculation of All the Popular Performance Evaluation Metrics Using the Python Functions
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