Thread by @ammaryh92

If you are planning to get into machine learning, then you are likely to use scikit-learn, one of Python's most famous libraries.
In this thread, I will try to break down scikit-learn's API which could be intimidating in the beginning.
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#MachineLearning

Estimators
- An estimator is any scikit-learn object that learns some parameters from data.
- All estimators implement "fit()" method to perform the estimation process.
- Estimators can also act as transformers or predictors.

Transformers
- They are estimators which use the parameters that they have learned to transform data.
- All transformers can implement "transform()" method to perform the transformation process.

I know this sounds vague, but it will become clearer after the following example.

Suppose we have a dataframe (a table) with some missing values in a particular column.
One way to tackle the missing values problem is to replace the missing values in each column with the mean of that column.
We can achieve this using scikit-learn's SimpleImputer object.

In that case, the SimpleImputer object acts as both an estimator and a transformer.
- It acts as an estimator because it first has to learn some parameters from data by calculating the mean of each column.

- It also acts as a transformer because it uses the learned parameters (each column's mean) to replace the missing value, thus transforming the dataframe.

Note:
Transformers can also implement a "fit_transform()" method which allows it to perform the estimation and the transformation process with one call, which is usually more computationally efficient.

Predictor
- Predictors are estimators which can learn some parameters from data (usually labeled data), and then use these learned parameters to predict new values.
- All predictors implement the "predict()" method to perform the prediction process.

For example, scikit-learn's LinearRegression object is an estimator and a predictor.
First, it estimate some parameters from labeled data (the slope and the intercept) using the "fit()" method.
Then, It uses the learned parameters to make predictions using the "predict()" method.

Predictors can also implement a "score()" method to measure the quality of the predictions.
The "score()" method takes as an input both the predicted value and the actual value, and measure the quality of the predictions using a specific performance measure that you can control.