What do you need to know about linear discriminant analysis?
LDA assumes that the predictors are normally distributed i.e. they come from gaussian distribution. Various classes have class specific means and equal covariance or variance. Under the MASS package, we have the lda () function for computing the linear discriminant analysis.
What are the arguments to the train function in caret?
We begin with a simple additive logistic regression. Here, we have supplied four arguments to the train () function form the caret package. form = default ~ . specifies the default variable as the response. It also indicates that all available predictors should be used.
Which is the best regression in the caret package?
7.0.24Logistic Regression 7.0.25Mixture Model 7.0.26Model Tree 7.0.27Multivariate Adaptive Regression Splines 7.0.28Neural Network 7.0.29Oblique Tree 7.0.30Ordinal Outcomes 7.0.31Partial Least Squares 7.0.32Patient Rule Induction Method 7.0.33Polynomial Model 7.0.34Prototype Models 7.0.35Quantile Regression
How is cross validated accuracy reported in caret?
The cross-validated accuracy is reported. Note that, caret is an optimist, and prefers to report accuracy (proportion of correct classifications) instead of the error that we often considered before (proportion of incorrect classifications). We see that there is a wealth of information stored in the list returned by train ().
In Linear Discriminant Analysis (LDA) we assume that every density within each class is a Gaussian distribution. Linear and Quadratic Discriminant Analysis: Gaussian densities. In LDA we assume those Gaussian distributions for different classes share the same covariance structure.
How are Gaussian densities used in Quadratic discriminant analysis?
Linear and Quadratic Discriminant Analysis: Gaussian densities. In LDA we assume those Gaussian distributions for different classes share the same covariance structure. In Quadratic Discriminant Analysis (QDA) we don’t have such a constraint. You will see the difference later.
How are generative learning algorithms used in Gaussian discriminant analysis?
Such algorithms try to model P (y|X) i.e. given a feature set X for a data sample what is the probability it belongs to the class ‘y’. On the other hand, Generative Learning Algorithms follow a different approach, they try to capture the distribution of each class separately instead of finding a decision boundary among classes.
Which is better Gaussian discriminant analysis or logistic regression?
Therefore, Gaussian Discriminant Analysis works quite well for a small amount of data (say a few thousand examples) and can be more robust compared to Logistic Regression if our underlying assumptions about the distribution of the data are true Reference: http://cs229.stanford.edu/notes2020spring/cs229-notes2.pdf