This page lists all the data preprocessing and transformation parameters in the setup
Scale and Transform
Other setup parameters
Datasets for various reasons may have missing values or empty records, often encoded as blanks or
NaN. Most of the machine learning algorithms are not capable of dealing with the missing values. Learn More.
Categorical features in the dataset contain the label values (ordinal or nominal) rather than continuous numbers. Most of the machine learning algorithms are not capable of handling categorical data without encoding. Learn More.
When the categorical features in the dataset contain variables with intrinsic natural order such as Low, Medium, and High, these must be encoded differently than nominal variables (where there is no intrinsic order for e.g. Male or Female). Learn More.
When categorical features in the dataset contain variables with many levels (also known as high cardinality features), then typical One-Hot Encoding leads to the creation of a very large number of new features. Learn More.
When categorical features in the dataset contain unseen variables at the time of predictions, it may cause problems for the trained model as those levels were not present at the time of training. Learn More.
Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to rescale the values of numeric columns in the dataset without distorting the differences in the ranges of values. Learn More.
While normalization rescales the data within new limits to reduce the impact of magnitude in the variance, Feature transformation is a more radical technique. Transformation changes the shape of the distribution. Learn More.
It is often seen in machine learning experiments when two features combined through an arithmetic operation become more significant in explaining variances in the data than the same two features separately. Learn More.
In machine learning experiments the relationship between the dependent and independent variable is often assumed as linear, however, this is not always the case. Sometimes the relationship between dependent and independent variables is more complex. Learn More.
When a dataset contains features that are related to each other in some way, for example, features recorded at some fixed time intervals, then new statistical features such as mean, median, variance, and standard deviation for a group of such features. Learn More.
Feature binning is a method of turning continuous variables into categorical values using the pre-defined number of bins. It is effective when a continuous feature has too many unique values or few extreme values outside the expected range. Learn More.
Sometimes a dataset can have a categorical feature (or multiple categorical features) that has a very high number of levels (i.e. high cardinality features). If such feature (or features) are encoded into numeric values, then the resultant matrix is a sparse matrix. Learn More.
Feature Selection is a process used to select features in the dataset that contributes the most in predicting the target variable. Working with selected features instead of all the features reduces the risk of over-fitting, improves accuracy, and decreases the training time. Learn More.