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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? \([0,0]\) \( [0,1]\) \([0,-2]\) \([0,2]\)

Lakshman Bhaiya edited in Machine Learning Feb 5

by Lakshman Bhaiya

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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.

squirrel69 commented in Machine Learning Feb 4

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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 ($\square$)?

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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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: $X_1, X_2, X_5$ $X_1, X_2, X_3, X_4, X_5, X_6$ $X_3, X_4$ $X_1, X_2, X_3, X_4$

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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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: \(2k + 1\) \(2^k + 1\) \(2^{k+1} + 1\) \(k^2 + 1\)

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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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

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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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.

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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

Lakshman Bhaiya recategorized in Machine Learning Feb 4

by Lakshman Bhaiya

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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:

rajveer43 answer selected in Machine Learning Feb 1

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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

rajveer43 answer selected in Machine Learning Feb 1

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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.

akhilasaivemula answered in Machine Learning Jan 31

by akhilasaivemula

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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? $− log(10)$ $−0.1 log(1)$ $− log(0.1)$ $−10 log(0.1)$

Shubham0100 answered in Machine Learning Jan 30

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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

redshellspy answered in Machine Learning Jan 30

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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

rajveer43 commented in Machine Learning Jan 28

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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: ... $\frac{\partial J}{\partial W^{[1]}} = (\hat{y} - y) \cdot \sigma'(z^{[1]}) \cdot X^T$

rajveer43 commented in Machine Learning Jan 28

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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.

rajveer43 answered in Machine Learning Jan 27

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