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Python is one of the most popular languages used in data science. It has a massive library that makes it easy for anyone to conduct machine learning and deep learning experiments. In this blog, we will be discussing regression and classification metrics with python Programming in AI/ML.  

We will show how to use some of these metrics to measure the performance of your models, which can help you make decisions about what algorithm or architecture might work best for your application or dataset!

What is a regression metric?

A regression metric measures how accurately a machine learning model predicts future values. To calculate a regression metric, you first need to collect predicted and actual values data. Then, you can use various measures to evaluate how well the model performs. 

How to use classification metrics with python Programming in AI/ML?

A classification metric or accuracy score measures how accurately a machine learning model predicts the correct class label for each data point in your training dataset. Once you have a classification metric, you can evaluate your machine learning model's performance. 

You can use many different classification metrics to measure performance for a classifier machine learning model. Common ones include accuracy score, precision, recall, actual positive rate, and recall at different false-positive rates. You can also calculate the Matthews correlation coefficient (MCC) to measure how well your model performs.

Accuracy Score:

Accuracy score measures how often the predicted value equals the actual value. It's also known as error rate, accuracy, or simply classification accuracy. You can calculate the accuracy score by dividing the total number of correct predictions from all predictions made.

Precision:

Precision is the number of correct predictions divided by the number of predictions made. 

Recall:

Recall, or valid positive rate is the number of correct predictions divided by the number of positives. You can calculate how well your model performs for different classes by plotting a ROC curve and calculating the AUC.

False Positive:

False-positive is also known as Type I Error or alpha error in statistical hypothesis testing. It's when your model predicts that an instance belongs to one class, but it belongs to another.

False Negative:

False-negative is also known as Type II Error or beta error in statistical hypothesis testing. It's when your model predicts that an instance belongs to one class but belongs to another, and the actual value isn't present in training data. 

Matthews Correlation Coefficient (MCC):

The Matthews correlation coefficient measures how well your model predicts the labels of unseen instances from training data. 

Area Under Curve (AUC):

The AUC score measures how well your model predicts future values by plotting a ROC curve and calculating the area under it.

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