#Python runner technique full#
#Python runner technique code#

The same is true for imputation by changing the parameters of SimpleImputer(), we can perform different imputation techniques with Scikit-learn, whereas Feature-engine has several transformers, each of which can perform at most 2 different imputation variations. Feature-engine, on the other hand, presents 3 different transformers for discretization. For example, we can apply 3 discretization techniques by simply changing the parameters of the KBinsDiscretizer() from Scikit-learn. Most Scikit-learn transformers are centralized, meaning that one transformer, can carry out different transformations. Should we do equal-width or equal-frequency discretization? Should we impute with the mean, median, or an arbitrary number? Sometimes, we don’t know which transformation technique returns the most predictive variable. The beauty of using Feature-engine’s SklearnWrapper, is that the output is a pandas dataframe! Versatility With Scikit-learn, we need to use a special transformer to slice the dataset into the desired group of variables.We can do this y using the ColumnTransformer or Feature-engine’s SklearnWrapper. With Feature-engine and Category encoders, we select the variables to transform within the transformer. The Python libraries offer the possibility to select the variables that we want to transform. We would apply certain imputation methods to numerical variables and other methods to categorical variables.

#Python runner technique full#

Input: Data slice vs full data setĭata scientists apply different feature engineering methods to different variable subsets.įor example, we would only impute variables with missing data, and not the entire data set.

#Python runner technique code#

If we choose to work with Scikit-learn, we may need to add a line of code or two to transform the NumPy arrays into Pandas dataframes to continue with data visualization. Pandas is a great tool for data analysis and visualization, and thus, libraries that return Pandas dataframes are inherently more data analysis “friendly”.

Often, we want to understand how the feature engineering transformations affect the variable distribution and their relationships with other variables. Pandas dataframes are better suited for data visualization. NumPy arrays are optimized for machine learning, as NumPy is more computationally efficient. Scikit-learn returns NumPy arrays instead. Main differences among Python packages for Feature Engineering - Created by the author Output: NumPy array vs Pandas dataframeįeature-engine and Category Encoders return pandas dataframes. I compare the following open-source Python libraries: In this article, I discuss Python implementations of feature engineering for machine learning. Feature engineering is crucial to training accurate machine learning models, but is often challenging and very time-consuming.įeature engineering involves imputing missing values, encoding categorical variables, transforming and discretizing numerical variables, removing or censoring outliers, and scaling features, among others. A large chunk of that time is spent on feature engineering.įeature engineering is the process of transforming and creating features that can be used to train machine learning models. Also, pass the cls as the first parameter to the class method.Feature engineering for machine learning with Python - Image from PixabayĪccording to Forbes, data scientists and machine learning engineers spend around 60% of their time prepping data before training machine learning models. Use decorator to change an instance method to a class method.Python class methods aren’t bound to any specific instance, but classes.

For example, the create_anonymous() is a factory method because it returns a new instance of the Person class. When a method creates an instance of the class and returns it, the method is called a factory method. In practice, you often use class methods for methods that create an instance of the class. You can use class methods for any methods that are not bound to a specific instance but the class.

The following table illustrates the differences between class methods and instance methods: Features Code language: Python ( python ) Class methods vs.