Applied Data Science Coding in Python: How to get Feature Importance In order to understand the importance of different features in a dataset, you can use a technique called “feature importance.” This technique allows you to see which features in your dataset have the biggest impact on the outcome or target variable. There are many …
Month: August 2019
Applied Data Science Coding in Python: Feature Extraction with Univariate Statistics “Feature Extraction with Univariate Statistics” is a technique used in Python to select the most informative features from a dataset. It is based on the statistical tests for the presence of a relationship between each feature and the target variable. The feature selection is …
Applied Data Science Coding in Python: Feature Extraction with RFE “Feature Extraction with RFE” is a technique used in Python to select the most informative features from a dataset. RFE stands for “Recursive Feature Elimination” and it is a method used to select the most relevant features from a dataset by recursively eliminating less important …
Applied Data Science Coding in Python: How to normalise data Normalizing data is a way of scaling the data so that it falls within a specific range. The most commonly used range is between 0 and 1. Normalizing data is useful in situations where the scale of the data can affect the performance of a …
Applied Data Science Coding in Python: How to standardize Data Standardizing data in Python using scikit-learn is a way to bring all of the features in your dataset to the same scale. This is useful because some machine learning models can be sensitive to the scale of the input features. The process of standardizing involves …
Applied Data Science Coding in Python: How to rescale Data Rescaling data is a technique used to transform the values of a dataset to be in a specific range. This is often done to make sure that data is on the same scale before applying machine learning algorithms. There are different ways to rescale data, …
Applied Data Science Coding in Python: How to do Binarization Binarization is the process of converting a continuous or numeric variable into a binary variable. The binary variable can take on only two values, for example, 0 and 1, true and false, or yes and no. This process is often used in machine learning and …