# Discrimination vs Calibration - Machine Learning Models

I came across a new term called Calibration while reading about prediction models.

Can you please help me understand how different it is from Discrimination.

We build ML models to discriminate two/more classes from one another

But what does calibration mean and what does it mean to say that "The model has good discriminative power but poorly calibrated/calibrative power?"

I thought we usually only look for separation only between 2 classes.

Can help me with this with a simple example please?

• Calibration is based on range theory and discrimination is based on mathematics - quadratic thereom . A range is interval. Math is exact - the truth.. – Subhash C. Davar Mar 13 at 16:17
• It seems that prediction models need a reinforcement by calibration of particular phenomenon e.g. occurrence of forest in the past. The postulated model needs a validation or affirmation- The model has good discriminative power but poorly calibrated/calibrative power? ML - the maximum likelihood- Likelihood Ratio are frequently used in the Calibration endeavor. The Discriminant function (mathematical) is utilized to understand or ascertain say, growth of a present forest over the time. The preceding comment is somewhat technical and deviates from your query. – Subhash C. Davar Mar 14 at 2:32