With Machine Learning
Feature scaling is a technique of normalizing or standardizing data into a certain range suitable for fitting a machine learning algorithm. If we don’t scale our data for example we have a variable called age with values in range 12
Read moreOne key challenge with most of the machine learning algorithm is the inability to work with categorical variables. They need the features to be converted to numeric form. The processing of transforming categorical features to numerical form is referred to
Read moreFeature selection is a feature engineering process (sometimes considered as data pre-processing method) of choosing the variables with high predictive power from the dataset. It can be conducted for both supervised and unsupervised learning. For supervised approach we statistically evaluate
Read moreWorking with data with high dimension often possess a challenge commonly referred to as the “curse of dimensionality”. It’s difficult to analyse, visualize and model high-dimensional data hence the need to transform the data from high-dimension to low-dimension. The process
Read moreFeature engineering is the process of using domain knowledge and scientific techniques to select and transform the most important features/variables that will yield an optimum machine learning or statistical model. The main goal of feature engineering is to increase the
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