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Trying to understand Model Evaluation Metrics? Then, this post will help you to get a firm understanding of it. 👉🏻 Check the post to know more and update your knowledge For more such useful content, follow @learnbaydatascience. Check the link in the bio to know more about our courses: https://bit.ly/3GZ7n4s 📲 DM for more details or queries #datascience #machinelearning #python #artificialintelligence #ai #data #dataanalytics #bigdata #programming #coding #deeplearning #technology

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Some curve fitting methods are given here using conversational mode. It shows that curves are not just only an art they talk to us, expresses something. That's why math is the language of mother nature.

Analytics Vidhya | Data Science Community on Instagram: "Pearson Correlation Coefficient (r), often denoted as r, measures the strength and direction of a linear relationship between two continuous variables. It ranges from -1 to 1. #analyticsvidhya #datascience #machinelearning #deeplearning #neuralnetworks #databases #data #datascientist #analyst #python #python3 #coding"

10 regression and classification loss functions in ML: (explained with proper usage) > Regression: 1) Mean bias error: captures the average bias in the prediction but is rarely used in training as negative and positive errors can cancel each other. 2) Mean absolute error: Measures the average absolute difference between predicted and actual value. One caveat is that small errors are as important as big ones. Thus, the magnitude of the gradient is independent of error size. 3) Mean squared...

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When learning how to find variance and standard deviation, find the average of your data set, then measure how far each value deviates from the mean.