Clustering
Mah
SVM
Ensembles
Formulas
100
What Kmeans stands for ?
how the first centroid in Kmeans are selected ?
- K : number of clusters, means:the averaging the data that is finding the centroids
- randomly, or manually
100
The problem of bagging that can be solved either :
A- statistical
B- computational
C- both
B
100
The cost parameter in the SVM means:
A) The number of cross-validations to be made
B) The kernel to be used
C) The tradeoff between misclassification and simplicity of the model
D) None of the above
C
100
10) Which of the following algorithm are not an example of ensemble learning algorithm?
A) Random Forest
B) Adaboost
C) Extra Trees
D) Gradient Boosting
E) Decision Trees
E
100
What is the formula for Precision?
True Positives / (True Positives + False Positives)
200
what is the method use to optimize the number of clusters? how it work?
Elbow method: plot of error vs number of clusters, and we choose the the elbow
or gridsearch -you show specify the error-
200
What is Bootstrapping ?
Sampling with replacement.
200
[True or False] If you remove the non-red circled points from the data, the decision boundary will change?
False
200
1) Which of the following is/are true about bagging trees?
- In bagging trees, individual trees are independent of each other
- Bagging is the method for improving the performance by aggregating the results of weak learners
A) 1
B) 2
C) 1 and 2
D) None of these
C
200
What is the formula for Recall?
True Positives / (True Positives + True Negatives)
300
How to select the number of clusters in the hierarchal clustering ?
Setting the threshold of average distance , then intersection between the threshold line and the dendrogram plot
300
What is the consequence of low inertia ?
Dense cluster
300
What would happen when you use very small C (C~0)?
A) Misclassification would happen
B) Data will be correctly classified
C) Can’t say
D) None of these
A
300
2) Which of the following is/are true about boosting trees?
- In boosting trees, individual weak learners are independent of each other
- It is the method for improving the performance by aggregating the results of weak learners
A) 1
B) 2
C) 1 and 2
D) None of these
B
300
What is the formula to calculate R-square?
1 - (Residual Sum of Squares/ Total Sum of Squares)
400
What the difference between DBSCAN, and Kmeans
In Kmeans you need to specify number of clusters but in DBSCAN you don't .
Kmeans label all the data, but DBSCAN don't.
400
What is the code for selecting all the columns for a table in SQL ?
SELECT *
From table_name;
400
When the C parameter is set to infinite, which of the following holds true?
A) The optimal hyperplane if exists, will be the one that completely separates the data
B) The soft-margin classifier will separate the data
C) None of the above
A
400
3) Which of the following is/are true about Random Forest and Gradient Boosting ensemble methods?
- Both methods can be used for classification task
- Random Forest is use for classification whereas Gradient Boosting is use for regression task
- Random Forest is use for regression whereas Gradient Boosting is use for Classification task
- Both methods can be used for regression task
A) 1
B) 2
C) 3
D) 4
E) 1 and 4
E
400
What is the formula for gini index?
1- sum of squared probability
500
What do the DBSCAN hyper-Parameter - eps and minPoints represent ?
eps: specifies how close points should be to each other to be considered a part of a cluster.
minPoints: the minimum number of points to form a dense region
500
Tell one pro and one con of boosting
Pros
- Achieves higher performance than bagging
- Can be used for classification and regression equally well.
- Easily handles mixed data types.
Cons
- Difficult and time consuming to properly tune hyper-parameters.
- Cannot be parallelized like bagging (bad scalability when huge amounts of data).
- More risk of overfitting compared to bagging.
500
What is SVC on sklearn's default kernel and C parameter?
rbf (gaussian) and 1.0
500
5) what will happen when the “max_depth” hyperparameter is very large in Decision Tree ?
Hint : two things happen
over-fitting and the classification will take a very long time to finish
500
what is the formula of silhouette coefficient
