10. Which of these methods can be used for classification problems? Circle all t
ID: 3322254 • Letter: 1
Question
10. Which of these methods can be used for classification problems? Circle all that apply. (a) Decision Trees (b) Generalized Additive Models (GAM) (c) K-means clustering (d) K-nearest neighbors (KNN) (e) LASSC (f) Linear Discriminant Analysis (LDA) (g) Logistic Regression (h) Principal Components Analysis (PCA) i) Quadratic Discriminant Analysis (QDA) G) Random Forests (k) Ridge Regression (1) Support Vector Machines (SVM) 11. Which of these methods can be used for regression problems? Circle all that apply. (a) Decision Trees (b) Generalized Additive Models (GAM) (c) K-means clustering (d) K-nearest neighbors (KNN) (e) LASSO (f) Linear Discriminant Analysis (LDA) (g) Logistic Regression (h) Principal Components Analysis (PCA) (i) Quadratic Discriminant Analysis (QDA) 6) Random Forests (k) Ridge Regression (1) Support Vector Machines (SVM) 12. Which of these methods are considered unsupervised learning techniques? Circle all that apply (a) Decision Trees (b) Generalized Additive Models (GAM) (c) K-means clustering (d) K-nearest neighbors (KNN) (e) LASSO (0) Linear Discriminant Analysis (LDA) (g) Logistic Regression (h) Principal Components Analysis (PCA) (i) Quadratic Discriminant Analysis (QDA) 0) Random Forests (k) Ridge RegressionExplanation / Answer
10. Methods suitable for classification problems are:
a)Decision Trees
d)K-Nearest neighbors(KNN)
f)Linear Discriminant Analysis
i)Quadratic Discriminant Analysis
j)Random forest
l)Support Vector machines
11)Unsupervised learning techniques:
b)Generalized Additive Models
e)LASSO
f)Logistic Regression
k)Ridge Regression
12. Unsupervised learning techniques:
c)K-Means clustering
h)Principal component analysis
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