How do you perform feature selection with numerical input data?
- # example of mutual information feature selection for numerical input data. from pandas import read_csv.
- from sklearn. model_selection import train_test_split.
- from sklearn.
- # load the dataset.
- # load the dataset as a pandas DataFrame.
- # retrieve numpy array.
- # split into input (X) and output (y) variables.
- y = dataset[:,-1]
Which is an example of a ML algorithm?
The majority of ML algorithms have a built-in attribute to view the relative importance of the features used during model learning. Some examples include: Linear Regression: coef_ gives the list of coefficients. Higher the coefficient, the more of an impact that input feature has on the target variable
When to use mutual information in numerical data?
Mutual information is straightforward when considering the distribution of two discrete (categorical or ordinal) variables, such as categorical input and categorical output data. Nevertheless, it can be adapted for use with numerical input and categorical output.
Why are errors so important in numerical analysis?
Almost every computation we do will involve errors in some way: \ We may be working with imperfect input (e.g. noisy data) \ We may not be able to represent our values exactly \ Some of our algorithms knowingly produce the wrong answer so they can run faster This is important because these errors can interact in non-obvious ways.
How to calculate absolute errors in numerical analysis?
Numerical Analysis 14 Easter Term 2018/19 UNIVERSITY OF CAMBRIDGE Absolute and relative errors Intro to fundamental concepts. More on errors later in the course. Let x be a real number. We will use x to denote its approximation. We de\\fne two ways of measuring error introduced by this approximation. Absolute error: \ x\\x xj