Recent questions and answers in Machine Learning / GATE Overflow for GATE CSE

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Memory Based GATE DA 2024 | Question: 36
In the context of K-Nearest Neighbors ($\mathrm{KNN}$), what is the minimum odd value of $\mathrm{K}$ such that the diamond ($\diamond$) shaped data point gets classified as ($\square$)?

In the context of K-Nearest Neighbors ($\mathrm{KNN}$), what is the minimum odd value of $\mathrm{K}$ such that the diamond ($\diamond$) shaped data point gets classified...

GO Classes

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GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 37
Consider the vectors: \[ \begin{aligned} & X_1=\begin{bmatrix} 1 \\ 0 \end{bmatrix}, \quad X_2=\begin{bmatrix} 0 \\ 1 \end{bmatrix}, \quad X_3=\begin{bmatrix} 0 \\ -1 \end{bmatrix}, \\ & X_4=\begin{bmatrix} -1 \\ 0 \end{bmatrix}, \ ... vectors? Select the correct option: $X_1, X_2, X_5$ $X_1, X_2, X_3, X_4, X_5, X_6$ $X_3, X_4$ $X_1, X_2, X_3, X_4$

Consider the vectors:\[\begin{aligned}& X_1=\begin{bmatrix} 1 \\ 0 \end{bmatrix}, \quad X_2=\begin{bmatrix} 0 \\ 1 \end{bmatrix}, \quad X_3=\begin{bmatrix} 0 \\ -1 \end{b...

GO Classes

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GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 38
Consider a scenario with $k$-binary attributes for a two-class classification task using Naive Bayes. What is the total number of parameters needed? Choose the correct option: $2k + 1$ $2^k + 1$ $2^{k+1} + 1$ $k^2 + 1$

Consider a scenario with $k$-binary attributes for a two-class classification task using Naive Bayes. What is the total number of parameters needed?Choose the correct o...

GO Classes

187 views

GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 39
Match the following: ...

Match the following:$$\begin{array}{|p{0.3\linewidth}|p{0.6\linewidth}|} \hline \textbf{Technique} & \textbf{Characteristic} \\ \hline Princip...

GO Classes

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GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 45
Consider a k-means clustering scenario with two vectors already present in Cluster 3: $\mathbf{v_1} = [1,1]$ and $\mathbf{v_2} = [-1,1]$. Which of the following vectors is also likely to be in Cluster 3? $[0,0]$ $ [0,1]$ $[0,-2]$ $[0,2]$

Consider a k-means clustering scenario with two vectors already present in Cluster 3: $\mathbf{v_1} = [1,1]$ and $\mathbf{v_2} = [-1,1]$. Which of the following vecto...

GO Classes

146 views

GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 48
In adversarial search, the evaluation function assigns High and Low High and High Low and Low Low and High

In adversarial search, the evaluation function assignsHigh and LowHigh and HighLow and LowLow and High

GO Classes

98 views

GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 56
ML question: Given four plots in options. Asking that which of the following are linearly separable.

ML question: Given four plots in options. Asking that which of the following are linearly separable.

GO Classes

94 views

GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 59
ML question: Bayesian Network

ML question: Bayesian Network

GO Classes

72 views

GO Classes asked Feb 4

1 votes

0 answers

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Memory Based GATE DA 2024 | Question: 61
ML Question: Given two Neural Networks involving weights. Asking for the values of p, q, r.

ML Question: Given two Neural Networks involving weights. Asking for the values of p, q, r.

GO Classes

197 views

GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 62
Machine learning question based on Information Gain

Machine learning question based on Information Gain

GO Classes

82 views

GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 63
Fisher discriminant analysis \[ J(u) = \frac{u^{\top} s_B u}{u^{\top} s_w u} \]

Fisher discriminant analysis\[ J(u) = \frac{u^{\top} s_B u}{u^{\top} s_w u} \]

GO Classes

78 views

GO Classes asked Feb 4

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Memory Based GATE DA 2024 | Question: 65
Dendogram single linkage

Dendogram single linkage

GO Classes

75 views

GO Classes asked Feb 4

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1 answer

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ML | DA Practice Questions
What is Error Analysis? (i) The process of analyzing the performance of a model through metrics such as precision, recall or F1-score. (ii) The process of scanning mis-classified examples to identify weaknesses of a model. (iii) The process ... to reduce the loss function during training. (iv) The process of identifying which parts of your model contributed to the error.

What is Error Analysis?(i) The process of analyzing the performance of a model through metrics such as precision, recall or F1-score.(ii) The process of scanning mis-clas...

akhilasaivemula

207 views

akhilasaivemula answered Jan 31

2 votes

2 answers

14

Classification | Neural Network | DA
Suppose we want to classify movie review text as (1) either positive or negative sentiment, and (2) either action, comedy, or romance movie genre. To perform these two related classification tasks, we use a neural network that shares ... Adding more layers to the neural network. Splitting the model into two with more overall parameters. Reduce the traning Data

Suppose we want to classify movie review text as (1) either positive or negative sentiment, and (2) either action, comedy, or romance movie genre. To perform these two re...

rajveer43

309 views

rajveer43 answered Jan 30

1 votes

2 answers

15

ML | DA | Multiclass Classification
Suppose a classifier predicts each possible class with equal probability. If there are 10 classes, what will the cross-entropy error be on a single example? $− log(10)$ $−0.1 log(1)$ $− log(0.1)$ $−10 log(0.1)$

Suppose a classifier predicts each possible class with equal probability. If there are 10 classes, what will the cross-entropy error be on a single example?$− log(10)$$...

Shubham0100

212 views

Shubham0100 answered Jan 30

1 votes

1 answer

16

ML | NN | DA Practice
Suppose that you are training a neural network for classification, but you notice that the training loss is much lower than the validation loss. Which of the following can be used to address the issue (select all that apply)? Use a network with fewer layers Decrease dropout probability √ Increase $L2$ regularization weight Increase the size of each hidden layer

Suppose that you are training a neural network for classification, but you notice that the training loss is much lower than the validation loss. Which of the following ca...

redshellspy

172 views

redshellspy answered Jan 30

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1 answer

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ML | DA Practice | Regularization
In the pursuit of achieving weight sparsity in a neural network, which regularization method should be your go-to choice? Options: A) L1 regularization B) L2 regularization C) Both L1 and L2 regularization D) No regularization needed for weight sparsity

In the pursuit of achieving weight sparsity in a neural network, which regularization method should be your go-to choice?Options:A) L1 regularizationB) L2 regularizationC...

Arjunmaniya

136 views

Arjunmaniya answered Jan 28

0 votes

1 answer

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Neural Network | ML | DA Practice
You have a single hidden-layer neural network for a binary classification task. The input is $X \in \mathbb{R}^{n \times m}$, output $\hat{y} \in \mathbb{R}^{1 \times m}$, and true label $y \in \mathbb{R}^{1 \times m}$. The forward propagation equations are: ... $\frac{\partial J}{\partial W^{[1]}} = (\hat{y} - y) \cdot \sigma'(z^{[1]}) \cdot X^T$

You have a single hidden-layer neural network for a binary classification task. The input is $X \in \mathbb{R}^{n \times m}$, output \(\hat{y} \in \mathbb{R}^{1 \times ...

Arjunmaniya

219 views

Arjunmaniya answered Jan 28

1 votes

1 answer

19

ML | DA | Classification
You encounter a classification task, and after training your network on 20 samples, the training converges, but the training loss is remarkably high. You decide to train the same network on 10,000 examples to address this issue. Is your approach to fixing ... of the model. D) No, a better approach would be to keep the same model architecture and increase the learning rate.

You encounter a classification task, and after training your network on 20 samples, the training converges, but the training loss is remarkably high. You decide to train ...

rajveer43

208 views

rajveer43 answered Jan 27

1 votes

2 answers

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AIMT-2 GATE DA 2024| ML Question for GATE | Wanshington Uni | Aug-23 Mid term test
Suppose we are performing leave-one-out (LOO) validation and $10$-fold cross validation on a dataset of size $100, 000$ to pick between $4$ different values of a single hyperparameter. How many times greater is the number of models that need to be trained for LOO validation versus $10$-fold cross validation? Answer:

Suppose we are performing leave-one-out (LOO) validation and $10$-fold cross validation on a dataset of size $100, 000$ to pick between $4$ different values of a single h...

prasantkr.singh

435 views

prasantkr.singh answered Jan 17

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