Recent questions tagged goclasses-da-dpp

Consider the following training dataset for a binary classification problem in $\mathbb{R}^2$. Each data-point is represented by $\mathbf{x}=\left[\begin{array}{ll}x_1 & ...

Consider a linearly separable dataset for a binary classification problem in $\mathbb{R}^d$. Three linear classifiers have been trained on this dataset. All three pass th...

Consider a data set $x_1=\left[\begin{array}{l}-1 \\ -1\end{array}\right], x_2=\left[\begin{array}{c}-1 \\ 0\end{array}\right], x_3=\left[\begin{array}{l}0 \\ 1\end{array...

  Blue line Brown line Both None of these

If the scores (i.e, $w^T x$ values) for some data points are $-4,3,1,2,-6$ respectively, what will be the probabilities returned for these points by Logistic Regression?$...

Given a linearly separable data set where the maximum Euclidean norm of any data point is $R=4$ (i.e., $\max \|x\|=4$), and the margin of separation is $\gamma=2$, what i...

  if the weight vector is multiplied by $-1$ , which classes will be predicted by the Perceptron for the data points $x_1$ and $x_2$ ?$x_1:+1, x_2:-1$ $x_1:-1, x_2:+1$ $x...

 Consider ten data points as shown in the following image:  The blue line represents the weight vector. As per this weight vector, the Perceptron algorithm will predict w...

Consider the following data set with three data points:$$\left(\left[\begin{array}{l}2 \\2\end{array}\right],+1\right),\left(\left[\begin{array}{c}2 \\-2\end{array}\right...

Consider the following data set:\[\begin{array}{|c|c|c|}\hlinef_1 & f_2 & y \\\hline-1 & -1 & -1 \\0 & 1 & +1 \\1 & 0 & +1 \\1 & 1 & +1 \\\hline\end{array}\]If the Percep...

Assume that the Perceptron algorithm is applied to a data set in which the maximum of the lengths of the data points is $4$ and the value of the margin ( $\gamma$ ) of th...

A binary classification dataset has 1000 data points belonging to $\{0,1\}^2$. A naive Bayes algorithm was run on the same dataset that results in the following estimate:...

A binary classification dataset has 1000 data points belonging to $\{0,1\}^2$. A naive Bayes algorithm was run on the same dataset that results in the following estimate:...

Consider the following binary classification dataset with two features $f_1$ and $f_2$. The data points given the labels follow the Gaussian distribution. The dataset is ...

Consider a binary classification dataset containing two features $f_1$ and $f_2$. The feature $f_1$ is categorical which can take three values and the feature $f_2$ is nu...

Consider the following binary classification dataset with two features $f_1$ and $f_2$. The data points given the labels follow the Gaussian distribution. The dataset is ...

Consider a binary classification dataset with two binary features, $f_1$ and $f_2$. The $f_2$ feature values are 0 for all label '$0$' examples but the label '$1$' exampl...

Consider a binary classification dataset contains only one feature and the data points given the label follow the given distribution$$\begin{aligned}& x \mid(y=0) \sim \m...

A naive Bayes model is trained on a dataset containing $d$ features $f_1, f_2, \ldots, f_d$. Labels are 0 and 1. If a test point was predicted to have the label 1 , which...

Which of the following statement is/are always correct in context to the naive Bayes classification algorithm for binary classification with all binary features? Here, $\...

Consider the two different generative model-based algorithms.1. Model 1: The chances of a feature occurring are affected by the occurrence of other features, and the mode...

Which of the following are valid decision regions for a decision tree classifier for datapoints in $\mathbb{R}^2$ ? The question in every internal node is of the form $f_...

Consider a binary classification problem in which all data points are in $R^2$. The red points belong to class +1 and the green points belong to class $-1$. A linear clas...

$p$ is the proportion of points with label $1$ in some node in a decision tree. Which of the following statements is/are true?As the value of $p$ increases from $0$ to $1...

$M i n=1$ $M i n=2$ $M a x=3$ $M a x=4$

What is the information gain for this split? Report your answer correct to three decimal places.

  What is the impurity in L1 if we use entropy as a measure of impurity? Report your answer correct to three decimal places.

If the algorithm is terminated at this level, then what are the labels associated with L1 and L2?L1: $0$ L1: $1$ L2: $0$ L2: $1$

Consider the following feature vectors:$$\mathbf{x}_1=\left[\begin{array}{c}1 \\2 \\1 \\-1\end{array}\right], \mathbf{x}_2=\left[\begin{array}{c}5 \\-3 \\-5 \\10\end{arra...

The blue and the red points belong to two different classes. Both of them are a part of the training dataset. The black point at $(1,2)$ also belongs to the training data...