Machine learning Questions And Answers With Complete
Solutions
------------ is an optimization technique that uses a randomly
selected subset of the data at every step rather than the full
dataset. This reduces the time spent calculating the derivatives
of the loss function. Correct Answers Stochastic gradient
descent
A ----------- shows the proportion of the total variance in the data
explained by each principal component. Correct Answers Scree
plot
A) ----------- is an example of unsupervised dimension reduction
algorithm.
B) ----------- is an example of supervised dimension reduction
technique.
C) ----------- are two of the most common unsupervised
clustering techniques. Correct Answers A) PCA B) Lasso
regression C) K-mean and hierarchical
As the order of polynomial model increases (more complex
model), the model bias will ----------- and model variance will
------------ Correct Answers decrease - increase
As we increase the hyperparameter K in a KNN model from 1 to
10, the model bias ----------------- and the model variance
---------------- Correct Answers increase - decrease
At the core of every CART model, is the need of the algorithm
to decide on two things:
, 1. Which feature to begin with!
2. Where to put the split!
(T/F) Correct Answers True
Boosting is a process that uses a set of machine learning
algorithms to combine weak learners (usually decision trees) to
form strong learners in order to increase the accuracy of the
model. (T/F) Correct Answers True
Bootstrapping the data plus using the aggregate to make a
decision is called? Correct Answers Bagging
By construction, Maximum Margin Classifier (MMC) is NOT
sensitive to outliers in the training data set. Correct Answers
False - MMCs are super sensitive to outliers in general
Cross validation allows us to compare different machine
learning methods and get a sense of how well they will work in
practice! (T/F) Correct Answers True
Eigenvectors show the direction of the principal components and
Eigenvalues represent their magnitudes. Loosely speaking the
eigenvectors are just the linear combinations of the original
variables. Their eigenvalues which are associated with each
principal component tell you how much variation in the data set
is explained. (T/F) Correct Answers True
Fitting the training data well but making poor predictions, is
called the ------------ Correct Answers bias-variance trade off
Solutions
------------ is an optimization technique that uses a randomly
selected subset of the data at every step rather than the full
dataset. This reduces the time spent calculating the derivatives
of the loss function. Correct Answers Stochastic gradient
descent
A ----------- shows the proportion of the total variance in the data
explained by each principal component. Correct Answers Scree
plot
A) ----------- is an example of unsupervised dimension reduction
algorithm.
B) ----------- is an example of supervised dimension reduction
technique.
C) ----------- are two of the most common unsupervised
clustering techniques. Correct Answers A) PCA B) Lasso
regression C) K-mean and hierarchical
As the order of polynomial model increases (more complex
model), the model bias will ----------- and model variance will
------------ Correct Answers decrease - increase
As we increase the hyperparameter K in a KNN model from 1 to
10, the model bias ----------------- and the model variance
---------------- Correct Answers increase - decrease
At the core of every CART model, is the need of the algorithm
to decide on two things:
, 1. Which feature to begin with!
2. Where to put the split!
(T/F) Correct Answers True
Boosting is a process that uses a set of machine learning
algorithms to combine weak learners (usually decision trees) to
form strong learners in order to increase the accuracy of the
model. (T/F) Correct Answers True
Bootstrapping the data plus using the aggregate to make a
decision is called? Correct Answers Bagging
By construction, Maximum Margin Classifier (MMC) is NOT
sensitive to outliers in the training data set. Correct Answers
False - MMCs are super sensitive to outliers in general
Cross validation allows us to compare different machine
learning methods and get a sense of how well they will work in
practice! (T/F) Correct Answers True
Eigenvectors show the direction of the principal components and
Eigenvalues represent their magnitudes. Loosely speaking the
eigenvectors are just the linear combinations of the original
variables. Their eigenvalues which are associated with each
principal component tell you how much variation in the data set
is explained. (T/F) Correct Answers True
Fitting the training data well but making poor predictions, is
called the ------------ Correct Answers bias-variance trade off
