You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
In linear regression, the model specification is that the dependent variable, y is a linear combination of the parameters (but need not be linear in the independent variables).
MultipleLinearRegression
MultivariateLinearRegression
LogisticRegression
It is a generalized linear model used for binomial regression.
Like many forms of regression analysis, it makes use of several predictor variables that may be either numerical or categorical.
For example, the probability that a person has a heart attack within a specified time period might be predicted from knowledge of the person's age, sex and body mass index.
Logistic regression is used extensively in the medical and social sciences as well as marketing applications such as prediction of a customer's propensity to purchase a product or cease a subscription.
Note : this should only be done after #2 and #7 are complete
The text was updated successfully, but these errors were encountered:
- move algorithms to `AI.Algorithms` instead of `AI.MachineLearning`
- move Machine to `AI.Machine` instead of repeating `AI.Machine.Machine`
- get rid of old `Regression.cs` file that's no longer needed, see #8 for updated notes
Multiple Linear Regression added in PR #19
Multivariate added in PR #19 but crashes Dynamo spectacularly - likely a data type issue (double[] vs double[][])
Add
MultipleLinearRegression
MultivariateLinearRegression
LogisticRegression
Note : this should only be done after #2 and #7 are complete
The text was updated successfully, but these errors were encountered: