Recent questions tagged machine-learning

Given below are two statements: one is labelled as Assertion A and the other is labelled as Reason RAssertion A: Unsupervised learning algorithms are used for tasks like ...

Given below are two statements: one is labelled as Assertion A and the other is labelled as Reason RAssertion A: In supervised learning, the model is trained using labell...

Q.27 Let $T : \mathbb{R}^3 \to \mathbb{R}^3$ be a linear map defined by\[T(x_1, x_2, x_3) = (3x_1 + 5x_2 + x_3,\; x_3,\; 2x_1 + 2x_3).\]Then the rank of $T$ is equal to _...

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For a classification problem, Principal Component Analysis (PCA) has been used to reduce the dimensionality of a feature space from $100$ to $10$.Which of the following o...

Consider that you are training a classifier for a $10$ -class classification problem. Each input is represented as a $512$ -dimensional vector. There are $1000$ samples, ...

In the following table, the Task column lists a few tasks related to machine learning. The Algorithm column lists a few algorithms.Each entry “t” from the Task column is ...

Consider that for a supervised learning task, the objective function being minimized is $f_{w}(x)=w x$, where $x \in \mathbb{R}$ is the input and $w \in \mathbb{R}$ is th...

Let four points in three-dimensional space be:$P1: [2,3,-1]$, $P2: [3,1,1]$, $P3: [5,-2,3]$ and $P4: [3,3,3]$.Hierarchical Agglomerative Clustering is used to cluster the...

Which of the following statements is true for Ridge Regression?The regularizer in the objective function of Ridge Regression is used to guard against scenarios where the ...

Consider that $20$ stories of Author $\text{X}$ and $10$ stories of Author $\text{Y}$ were kept together without mentioning the names of the authors. $\text{A}$ classifie...

Consider that Linear Ridge Regression is being used to learn a prediction function $y_{\text {pred }}=w^{T} x$, where $w, x \in \mathbb{R}^{2}$ and Mean Absolute Error (M...

Consider a fully-connected feed-forward multi-layer perceptron. It has $30$ neurons in the input layer, followed by two hidden layers and an output layer. The first hidde...

Consider a linear model $$y_{\text{pred}} = w^T x$$with Mean Absolute Error (MAE) loss and $L_2$ regularization..Given:\[\lambda = 0.20, \quad w = \begin{bmatrix} -3 \\ 4...

There are $20$ books of author $X$ and $10$ books of author $Y$, all placed together at one place without labels. $6$ books of author $X$ are classified as author $Y$. $2...

Let $M=I_n-\frac{1}{n}\mathbf{1}\mathbf{1}^T$, where $\mathbf{1}=(1,1,\dots,1)^T\in\mathbb{R}^n$.Consider the optimization problem $\max_{x} (x^T A x)$ subject to $x^T x=...

Four points in $3$-dimensional space are given as $P_1=(2,3,-1)$, $P_2=(3,1,1)$, $P_3=(5,-2,3)$, and $P_4=(3,3,3)$.Using Manhattan distance $(\mathbf{L1}$ distance$)$, wh...

Consider a fully connected feedforward neural network with $30$ input neurons, a first hidden layer with $4$ neurons, a second hidden layer with $3$ neurons, and an outpu...

Let $x_1,x_2,\dots,x_{100}$ be real-valued observations with sample mean$$\bar{x}=\frac{1}{100}\sum_{i=1}^{100}x_i$$The pairwise squared distance summation is $$S=\sum_{i...

Consider a $\mathbf{10}$-class classification problem with a dataset containing $\mathbf{1000}$ samples.Out of these:$\mathbf{100}$ samples are reserved as test data, and...

Which of the following statements is/are true for Ridge Regression?Ridge Regression aims to reduce the number of parameters that have negative values. The regularizer in ...

Principal Component Analysis (PCA) is applied to reduce the dimensionality of data from $\mathbf{1 0 0}$ dimensions to $\mathbf{1 0}$ dimensions.Let the angle between the...