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Most viewed questions in Artificial Intelligence
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61
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 − 1] T , what is h(x) explicitly as a function of x? write solution for It.
rajveer43
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Artificial Intelligence
Dec 11, 2023
by
rajveer43
333
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artificial-intelligence
machine-learning
0
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1
answer
62
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 been used? (a) L0 norm (b) L1 norm (c) L2 norm (d) either (A) or (B) (e) any of the above
rajveer43
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in
Artificial Intelligence
Jan 13
by
rajveer43
266
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machine-learning
artificial-intelligence
statistics
0
votes
1
answer
63
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 likely reduce test set error (b) decreasing $σ$ will most likely reduce test set error (C) not enough information is provided to determine how $σ$ should be changed
rajveer43
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in
Artificial Intelligence
Jan 15
by
rajveer43
264
views
machine-learning
statistics
artificial-intelligence
0
votes
2
answers
64
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) pick any of the 10 models you built for your model; use its error estimate on ... a new model on the full data set, using the \( \theta \) you found; use the average CV error as its error estimate
rajveer43
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in
Artificial Intelligence
Jan 13
by
rajveer43
261
views
machine-learning
artificial-intelligence
statistics
0
votes
1
answer
65
Machine Learning Self-doubt
Please Solve this question with full explanation.
gateexplore
asked
in
Artificial Intelligence
Nov 30, 2023
by
gateexplore
260
views
machine-learning
self-doubt
0
votes
1
answer
66
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, \(H()\) means entropy, and \(P()\) means probability) (a) \(P(Y | X_j)\) (b) \(P(Y) - P(Y | X_j)\) (c) \(H(Y) - H(Y | X_j)\) (d) \(H(Y | X_j)\) (e) \(H(Y) - P(Y)\)
rajveer43
asked
in
Artificial Intelligence
Jan 16
by
rajveer43
223
views
artificial-intelligence
machine-learning
statistics
probability
0
votes
1
answer
67
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 $X$. X Y Red True Green False Brown False Brown False (a) 1/2 (b) 1 (c) 3/2 (d) 2 (e) none of the above
rajveer43
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in
Artificial Intelligence
Jan 16
by
rajveer43
222
views
artificial-intelligence
statistics
machine-learning
binary-tree
0
votes
1
answer
68
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. “What country are you from) in addition to binary questions, they will in general tend to add the multiple answer questions to the tree before adding the binary questions
rajveer43
asked
in
Artificial Intelligence
Jan 13
by
rajveer43
222
views
algorithms
artificial-intelligence
machine-learning
0
votes
1
answer
69
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 validation (LOOCV) generally gives less accurate estimates of true test error than 10-fold ... following Statements is/are correct? Only P1 is True Only P2 is True P1 is True and P2 is False Both are False
rajveer43
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in
Artificial Intelligence
Jan 13
by
rajveer43
195
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machine-learning
artificial-intelligence
statistics
0
votes
0
answers
70
Memory Based GATE DA 2024 | Question: 32
Consider two admissible heuristic functions, \(h_1\) and \(h_2\). Determine which of the following combinations are admissible: \(\frac{h_1}{h_2}\) \(\left(h_2 > 0\right)\) \\ \(h_1 \cdot \tilde{h}_2\) \\ \(\left| h_1 - h_2 \right|\) \\ \(h_1 + h_2\)
GO Classes
asked
in
Artificial Intelligence
Feb 4
by
GO Classes
161
views
gate2024-da-memory-based
goclasses
artificial-intelligence
0
votes
1
answer
71
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 effect of increasing \( p ... , decreases variance (c)] Decreases bias, increases variance (d)] Decreases bias, decreases variance (e)] Not enough information to tell
rajveer43
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Artificial Intelligence
Jan 13
by
rajveer43
155
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0
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0
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72
What resources can i use to study the Data Warehousing part for the GATE DA paper?
Ameya Kulkarni
asked
in
Artificial Intelligence
Jan 30
by
Ameya Kulkarni
150
views
0
votes
1
answer
73
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 important to used unbiased estimators when one has smaller numbers of training examples. Which of the following statement( ... $P1$ and $P2$ are true (C) Only $P2$ is True (D) Both $P1$ and $P2$ are False
rajveer43
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in
Artificial Intelligence
Jan 13
by
rajveer43
138
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machine-learning
artificial-intelligence
statistics
0
votes
0
answers
74
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 estimate \( \hat{\mathbf{w}}_{\text{MLE}} \) do you think the ridge regression \( L_2 \) estimate \( \hat{\mathbf{w}}_{\text{ridge}} \) will be? (a)] 2 b)] 1 (c)] 0.666 (d)] 0.5
rajveer43
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in
Artificial Intelligence
Jan 13
by
rajveer43
128
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artificial-intelligence
machine-learning
statistics
0
votes
1
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75
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 effect of increasing \( \lambda \) ... bias, decreases variance (c)] Decreases bias, increases variance (d)] Decreases bias, decreases variance (e)] Not enough information to tell
rajveer43
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in
Artificial Intelligence
Jan 13
by
rajveer43
125
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artificial-intelligence
machine-learning
statistics
0
votes
1
answer
76
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 of the errors. To compute each error, we need to build a model with data of size $N2$, and test the ... $N1 = 10, N2 = 100, N3 = 10$ (d) $N1 = 10, N2 = 100, N3 = 10$
rajveer43
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Artificial Intelligence
Jan 13
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rajveer43
124
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machine-learning
artificial-intelligence
0
votes
0
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77
Memory Based GATE DA 2024 | Question: 50
You are provided with three images, each depicting a different face of a six-sided dice. Based on these images, determine the correct option.
GO Classes
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Artificial Intelligence
Feb 4
by
GO Classes
123
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gate2024-da-memory-based
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