An equation in maths has equal to separating the left and right side parts. The equation gives results based on input. Likewise, in machine learning models, the outcome’s performance is based on the input data you supplied to it.

## ML Vs Mathematical Equation.

Understand ML model with a math equation. That provides you with basic knowledge of the ML model.

- Math equation
- ML model with data

## 1. Math 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. ML Model

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.

### Machine learning models

Here are the seven best examples that you could consider ML models.

- 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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