In ML, the model is the heart. It mirrors a mathematical equation. The equation responds based on the input parameter. The accuracy of output defines the model’s effectiveness. This post tells you how to relate the ML Model and Mathematical equation.

## ML Vs Mathematical Equation.

This post tells the differences between these in two steps.

- Simple Mathematical Formula
- Model with Actual data

## 1.Simple Mathematical Formula

**Y = F(X) + e**

Here, the ‘Y’ is the target. The F(X) is actual data. And, you can call ‘e’ as an error, which is not related the function F. The features list is** X1, X2, X3…….Xn.** Here is Machine learning terminology.

As a part of Model building, all the learners, each developer should understand that each model contains relevant data and junk or useless data.

Here, the ‘e’ is useless Data. So the Model should use the right data to arrive at a decision. Usually, people clap the Model when the predicted result is close to the true value.

## 2. Model with Actual and Junk Data

Once the model is ready with a variety of data loaded, the next thing is you need to test it. Just give some input and get the output. If the output is as expected, then you can put that model for real-time use.

### Best Examples for ML.

Prediction is the most common use of ML. The best examples are:

- Deciphering handwritten digits or voice recordings
- Predicting the stock market
- Forecasting
- Predicting which users are most likely to click, convert, or buy
- Predicting which users will need product support and which are likely to unsubscribe
- Determining which transactions are fraudulent
- Making recommendations

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