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IIT M FOUNDATION DIPLOMA AN EXAM - 01 Sep 2024

MLP End Term37 questions100 marks19 MCQ · 13 MSQ · 5 SAFoundation / Diploma / afternoon

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Comprehension passage - the questions below it refer to this

Consider the following common data and answer the subquestion: question figure

Consider the following common data and answer the subquestion:

Q1MSQ2 marksselect all that apply

Consider the following code snippet: question figure

Consider the following code snippet: Which of the following will be equivalent to the above code snippet?

Q2SA2 marks

What is the output of the following code snippet: question figure

What is the output of the following code snippet: Enter -1, if you think the above statement will generate an error.

Your numeric answer:

Q3SA2 marks

What is the output of the following code snippet: question figure

What is the output of the following code snippet: Enter -1, if you think the above statement will generate an error.

Your numeric answer:

Q4SA2 marks

What is the output of the following code snippet: question figure

What is the output of the following code snippet: Enter -1, if you think the above statement will generate an error.

Your numeric answer:

Q5MCQ2 marks

What is the output of the following code snippet: question figure

What is the output of the following code snippet: Choose correct options from following:

The number of rows will decrease in the dataset.
The number of columns will decrease in the dataset.
The number of columns and number of rows, both, will decrease in thedataset.
There will be no change.
Insufficient information.

Q6MCQ2 marks

To load datasets from openml.org, which method will be appropriate?

load_openml()
read_openl()
read_data()
fetch_openml()
load_data()
load_csv()
fetch_csv()

Q7MCQ2 marks

In which of the option given below data preprocessing is required?

In some columns which has values between 0 and 1.
A column contains entity names such as ‘Chennai’, ‘CHENNAI’ and ‘MADRAS’.
The data has only numbers in all the columns.

Q8MCQ2 marks

Q9MCQ2 marks

Which of the following scikit-learn classes is best suited for examining how the number of samples impacts training and testing errors?

Q10MCQ2 marks

Consider following two statements: Statement 1: The GaussianNB classifier can incrementally learn using partial_fit. Statement 2: GaussianNB performance suffers when features are dependent, as it assumes independence in calculating conditional probabilities.

Both statements are True
Only statement 1 is True
Only statement 2 is True
Both statements are False

Q11MCQ2 marks

Q12MCQ2 marks

Which of the following is true for a hard margin SVM algorithm ?

It does not create hyperplanes as a decision boundary
It will correctly classify all the data point if the data is linearly separable
It is robust to outliers
It is mostly used for clustering the data

Q13MCQ2 marks

Q14MCQ2 marks

Decision Trees are prone to:

Low bias, low variance
High bias, low variance
Low bias, high variance
High bias, high variance

Q15MCQ3 marks

Q16MCQ3 marks

‘l1’, ‘l2’
‘l1’, None
‘l2’, ‘l1’
‘l2’, None

Q17MCQ3 marks

True
False
Yes
No
None of these

Q18MCQ3 marks

It controls the number of weak learners.
It shrinks the contribution of each classifier.
It sets the weight of each weak learner.
It adjusts the speed at which the model learns.

Q19MCQ4 marks

Output score is likely to increase.
Output score is likely to decrease.
Output score may increase or decrease.
Output score will remain the same.

Q20MCQ4 marks

Q21MCQ4 marks

The final predictions are based on the majority vote.
The final predictions are based on the average of probabilities predicted byeach classifier.
The final predictions are based on the weighted sum of the predictions.
The final predictions are based on the classifier with the highest accuracy.

Q22MCQ4 marks

The mean accuracy across all cross-validation folds.
The mean precision across all cross-validation folds.
The mean recall across all cross-validation folds.
The mean F1 score across all cross-validation folds.

Q23MCQ4 marks

Plotting the sum of the squared distances of samples to their closest clustercenter
by Changing the distance metric for KMeans
By initialising clustering centroids very far from each other
All of these

Q24MSQ2 marksselect all that apply

Q25MSQ2 marksselect all that apply

Q26MSQ2 marksselect all that apply

Which of the following is a hyper parameter?

Q27MSQ2 marksselect all that apply

Which of the following models are inherently multiclass models?

Perceptron
DecisionTreeClassifier
KNeighborClassifier
LogisticRegression

Q28MSQ2 marksselect all that apply

Which of the following class(es) is/are used to instantiate a neural network in Sklearn.

SGDClassifier()
MLPClassifier()
NNClassifier()
MLPRegressor()

Q29MSQ3 marksselect all that apply

Which of the following is correct?

Q30MSQ3 marksselect all that apply

Which of the following processes can be done if we have a dataset with imbalanced target class distribution?

Remove all the minority classes
Up-sample the minority classes
Remove all the majority classes
Down-sample the majority classes
create synthetic samples to balance the classes

Q31MSQ3 marksselect all that apply

Which of the following is true about Naive Bayes algorithm ?

It is primarily used for regression problems
It is primarily used for classification problems
Hyperparameter tuning is required
Hyperparameter tuning is not required

Q32MSQ3 marksselect all that apply

Which of the following is/are correct regarding RadiusNeighborsClassifier

Only 5 neighbours in the range of some radius are used to compute the labelof a sample.
All the neighbours in the range of some radius are used to compute the labelof a sample.
It is sensitive to outliers.
It is not sensitive to outliers.

Q33MSQ3 marksselect all that apply

Data is continuously being generated
Data is generated every month
Whole data is generated and its in a huge file size
For very small dataset

Q34MSQ4 marksselect all that apply

The neural network contains 3 hidden layers with 5 neurons in each hiddenlayer
The neural network contains 5 hidden layers with 3 neurons in each hiddenlayer
The neural network contains 2 hidden layers with 3 neurons in the secondhidden layer
The neural network contains 2 hidden layers with 5 neurons in the first hiddenlayer
All of the given options are correct

Q35MSQ5 marksselect all that apply

Number of samples at node N = 15. If it is split, it will result in 9 samples in theleft child node and 6 sample in the right child node.
Number of samples at node N = 5. If it is split, it will result in 4 samples in theleft child node and 2 samples in the right child node.
Number of samples at node N = 12. If it is split, it will result in 3 samples in theleft child node and 9 samples in the right child node.
Number of samples at node N = 7. If it is split, it will result in 4 samples in theleft child node and 3 samples in the right child node.

Q36SA2 marks

Your numeric answer:

Q37SA4 marks

Your numeric answer:

Answer key

Q1 (MSQ, 2 marks): A, C
Q2 (SA, 2 marks): 120
Q3 (SA, 2 marks): 3
Q4 (SA, 2 marks): -1
Q5 (MCQ, 2 marks): A
Q6 (MCQ, 2 marks): D
Q7 (MCQ, 2 marks): B
Q8 (MCQ, 2 marks): B
Q9 (MCQ, 2 marks): D
Q10 (MCQ, 2 marks): A
Q11 (MCQ, 2 marks): C
Q12 (MCQ, 2 marks): B
Q13 (MCQ, 2 marks): B
Q14 (MCQ, 2 marks): C
Q15 (MCQ, 3 marks): E
Q16 (MCQ, 3 marks): A
Q17 (MCQ, 3 marks): A
Q18 (MCQ, 3 marks): B
Q19 (MCQ, 4 marks): A
Q20 (MCQ, 4 marks): D
Q21 (MCQ, 4 marks): B
Q22 (MCQ, 4 marks): A
Q23 (MCQ, 4 marks): A
Q24 (MSQ, 2 marks): A, D
Q25 (MSQ, 2 marks): A, D
Q26 (MSQ, 2 marks): B, C, D
Q27 (MSQ, 2 marks): B, C
Q28 (MSQ, 2 marks): B, D
Q29 (MSQ, 3 marks): A, D
Q30 (MSQ, 3 marks): B, D, E
Q31 (MSQ, 3 marks): B, D
Q32 (MSQ, 3 marks): B, D
Q33 (MSQ, 3 marks): A, B, C
Q34 (MSQ, 4 marks): C, D
Q35 (MSQ, 5 marks): A, D
Q36 (SA, 2 marks): 0.319 to 0.328
Q37 (SA, 4 marks): 180

Concepts this paper tests

Auto-generated summary from the source site. Handy as a checklist; the lessons above explain each idea from scratch.

Here’s a structured summary of core topics, concepts, and equations for the Diploma Level: Machine Learning Practice (Computer-Based Exam), along with Mermaid knowledge graphs to visualize relationships.

1. Core Topics & Concepts

A. Pandas & Data Manipulation

DataFrame Operations:
- Indexing: df.loc[] (label-based), df.iloc[] (position-based).
- Slicing: df.loc[23:24, 'Maths':'History'] ≡ df.iloc[2:4, 1:3] (Q2).
- Handling missing data: df.dropna() reduces rows (Q6).
- Data types: df.Hindi.apply(type).nunique() counts unique types (e.g., int, float, str) (Q4).
Chunked Reading:
- pd.read_csv(..., chunksize=1024) for large files (Q12).

B. Scikit-Learn Workflow

Data Splitting:
- train_test_split(test_size=30) for exact sample counts (Q25).
- ShuffleSplit for randomized splits (Q26).
Cross-Validation:
- KFold(n_splits=3) splits data into folds (Q9).
- cross_val_score evaluates models (Q26).
Learning Curves:
- sklearn.model_selection.learning_curve analyzes training/test errors vs. sample size (Q10).
Hyperparameter Tuning:
- GridSearchCV exhaustively searches parameter grids (Q16, Q21).
- Example: param_grid = {'max_depth': [2, 3, 4]}.

C. Models & Algorithms

Linear Models:
- SGDRegressor/Classifier:
  - loss='log_loss' (logistic regression), loss='hinge' (SVM), loss='perceptron' (Q30).
  - penalty='l1', 'l2', or 'elasticnet' for regularization (Q17).
  - partial_fit for online learning (Q34).
- Polynomial Features:
  - PolynomialFeatures(degree=2) in pipelines (Q16).
Tree-Based Models:
- Decision Trees:
  - Prone to high variance/low bias (overfitting) (Q15).
  - Hyperparameters: max_depth, min_samples_split, min_samples_leaf (Q20, Q36).
- Random Forest/Bagging:
  - BaggingClassifier with DecisionTreeClassifier as base estimator (Q23).
Naive Bayes:
- Assumes feature independence; suffers with dependent features (Q11).
- No hyperparameter tuning typically required (Q32).
Neural Networks:
- MLPClassifier/MLPRegressor for multi-layer perceptrons (Q29, Q35).
- hidden_layer_sizes=(5,3) defines 2 hidden layers (5 and 3 neurons) (Q35).
Clustering:
- K-Means:
  - Inertia (sum of squared distances) → 0 when n_clusters = n_samples (Q38).
  - Elbow method: Plot inertia vs. n_clusters (Q24).
Ensemble Methods:
- Voting Classifier:
  - voting='soft' averages predicted probabilities (Q22).
- AdaBoost:
  - learning_rate controls weak learner contributions (Q19).
- Radius Neighbors:
  - Uses all neighbors within a radius (unlike KNN’s fixed k) (Q33).
Imbalanced Data:
- Strategies: Upsampling minority, downsampling majority, or synthetic samples (SMOTE) (Q31).

D. Evaluation Metrics

Regression:
- mean_absolute_error, neg_mean_squared_error (Q26, Q37).
Classification:
- Accuracy, precision, recall, F1 (Q22).
Cross-Validation:
- cross_val_score(scoring='neg_r2') for regression (Q26).

E. Text Processing

CountVectorizer:
- vocabulary_ maps terms to indices (Q14).
- Example: {'this': 4, 'is': 2, ...}.

F. Model-Specific Notes

SVM:
- Hard-margin SVM classifies all points correctly if data is linearly separable (Q13).
Perceptron:
- SGDClassifier(loss='perceptron') is its stochastic version (Q30).
Logistic Regression:
- SGDClassifier(loss='log_loss') (Q30).

2. Key Equations (Math-Focused)

A. Linear Models

SGD Update Rule:

w_{t+1} = w_t - \eta \cdot \nabla J(w_t)

$\eta$ : Learning rate (eta0 in SGDRegressor).
$J(w)$ : Loss function (e.g., MSE for regression, log loss for classification).

Regularization (L1/L2):
- L1 (Lasso):

\text{Penalty} = \alpha \sum |w_i|

L2 (Ridge):

\text{Penalty} = \alpha \sum w_i^2

Elastic Net:

\text{Penalty} = \alpha \left( \rho \sum |w_i| + \frac{1-\rho}{2} \sum w_i^2 \right)

B. Decision Trees

Gini Impurity:

G = 1 - \sum p_i^2

Entropy:

H = -\sum p_i \log_2 p_i

C. K-Means

Inertia (Objective Function):

\text{Inertia} = \sum_{i=1}^n \min_{\mu_j \in C} ||x_i - \mu_j||^2

$\mu_j$ : Cluster centroids.
Minimized when $n\_clusters = n\_samples$ (inertia = 0).

D. Evaluation Metrics

Mean Absolute Error (MAE):

\text{MAE} = \frac{1}{n} \sum |y_i - \hat{y}_i|

R² Score:

R^2 = 1 - \frac{\sum (y_i - \hat{y}_i)^2}{\sum (y_i - \bar{y})^2}

E. Naive Bayes

Conditional Probability (Gaussian NB):

P(x_i | y) = \frac{1}{\sqrt{2\pi\sigma_y^2}} \exp\left(-\frac{(x_i - \mu_y)^2}{2\sigma_y^2}\right)

Graph 2: Model Relationships

Graph 3: Data Preprocessing

Graph 4: Model Evaluation

Graph 5: Hyperparameter Tuning

4. Exam Tips

Pandas:
- loc vs. iloc: loc uses labels, iloc uses integer positions.
- dropna() affects rows by default (not columns unless axis=1).
Scikit-Learn:
- Pipelines: Use make_pipeline to chain transforms and estimators.
- Partial Fit: For online learning (e.g., SGDRegressor.partial_fit).
- Voting: voting='soft' averages probabilities; 'hard' uses majority vote.
Math:
- Know when inertia = 0 in K-Means (n_clusters = n_samples).
- Understand how learning_rate affects AdaBoost (shrinks contributions).
Code Snippets:
- Memorize common patterns (e.g., KFold output, CountVectorizer vocabulary).
Hyperparameters:
- max_depth, n_neighbors, degree are hyperparameters (not intercept_).

Good luck! Focus on practical coding patterns, math behind algorithms, and scikit-learn’s API.