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Highest voted questions in Artificial Intelligence

#42
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AI Sample Question for DS-AI
Imagine you are guiding a robot through a grid-based maze using the A* algorithm. The robot is currently at node A (start) and wants to reach node B (goal). The heuristi...
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#43
313
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1 answers
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UPENN | DS-AI Sample | Decision Tree
When choosing one feature from \(X_1, \ldots, X_n\) while building a Decision Tree, which of the following criteria is the most appropriate to maximize? (Here, \(H()\) me...
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#44
358
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UPENN | ML | DECISION TREE
Given the following table of observations, calculate the information gain $IG(Y |X)$ that would result from learning the value of $X$. XYRedTrueGreenFalseBrownFalseBrownF...
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#45
340
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UPENN | ML Questions for GATE DA
In fitting some data using radial basis functions with kernel width $σ$, we compute training error of $345$ and a testing error of $390$.(a) increasing $σ$ will most li...
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#46
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DA Practice | UPENN | ML | Naive Bais
Suppose you have a three-class problem where class label \( y \in \{0, 1, 2\} \), and each training example \( \mathbf{X} \) has 3 binary attributes \( X_1, X_2, X_3 \in ...
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#47
348
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2 answers
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UPENN | ML | Cross validation
Suppose you have picked the parameter \( \theta \) for a model using 10-fold cross-validation. The best way to pick a final model to use and estimate its error is to(a) p...
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#48
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Decision Tree | Sample Question
$True$ or $False?$ If decision trees such as the ones we built in class are allowed to have decision nodes based on questions that can have many possible answers (e.g. �...
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#49
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UPENN | ML | Cross Validation
P1: In the limit of infinite training and test data, consistent estimators always give at least as low a test error as biased estimators. P2: Leave-one out cross validati...
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#50
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UPENN | ML | DA Practice | Regularization
After applying a regularization penalty in linear regression, you find that some of the coefficients of $w$ are zeroed out. Which of the following penalties might have be...
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#51
174
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UPENN | ML | DA Practice
Using the same data as above \( \mathbf{X} = [-3, 5, 4] \) and \( \mathbf{Y} = [-10, 20, 20] \), assuming a ridge penalty \( \lambda = 50 \), what ratio versus the MLE es...
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#52
205
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UPENN | ML | DA Practice
Consider the statements:$P1:$ It is generally more important to use consistent estimators when one has smaller numbers of training examples.$P2:$ It is generally more imp...
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#53
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DA Practice | UPENN | ML | Bias-Variance Trade Off | Regularization
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + k \|\mathbf{w}\|_p^p. \] What is the eff...
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#54
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UPENN | ML | DA Practice | Bias-Variance Trade-Off
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + \lambda \|\mathbf{w}\|_1. \] What is the...
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#55
208
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UPENN | Midterm | K Fold Validation | DA Practice |
Suppose we want to compute $10-Fold$ Cross-Validation error on $100$ training examples. We need to compute error $N1$ times, and the Cross-Validation error is the average...
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#56
421
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Ai Questions | DS-AI Paper | GATE 2024
Given a tree with a branching factor of 3 and a depth of 4, calculate the maximum number of nodes expanded during a breadth-first search.
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#57
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GATE DS-AI questions | ML
Consider the feature transform z = [L0(x) L1(x) L2(x)]T with Legendre polynomials and the linear model h(x) = w T .z. For the regularized hypothesis with w = [−1 + 2 �...
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#58
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Machine Learning Self-doubt
Please Solve this question with full explanation.
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#59
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UGC NET CSE | June 2016 | Part 3 | Question: 66
A perceptron has input weights $W_1=-3.9$ and $W_2=1.1$ with threshold value $T=0.3.$ What output does it give for the input $x_1=1.3$ and $x_2=2.2?$$-2.65$$-2.30$$0$$1$
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#60
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UGC NET CSE | June 2016 | Part 3 | Question: 75
A software program that infers and manipulates existing knowledge in order to generate new knowledge is known as:Data dictionaryReference mechanismInference engineControl...
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