MCQs

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Overall MCQs for Reference

What is the main objective of splitting a dataset in machine learning?
a. To improve storage efficiency
b. To assess and enhance model performance
c. To encrypt data
d. To reduce model complexity
Answer: b. To assess and enhance model performancealooba+1
Which technique divides data into training and testing groups?
a. Feature Engineering
b. Label Encoding
c. Train-Test Split
d. Vectorization
Answer: c. Train-Test Splitsanfoundry+2
What is the most common split ratio for train-test split?
a. 90:10
b. 80:20
c. 70:30
d. 60:40
Answer: b. 80:20realpython+1
When handling imbalanced datasets, which split strategy is recommended?
a. Random Split
b. Stratified Splitting
c. Sequential Split
d. Manual Split
Answer: b. Stratified Splittingbuiltin+1
What does K-Fold Cross Validation mainly help reduce in model evaluation?
a. Underfitting
b. Overfitting
c. Bias
d. Variance
Answer: d. Variancegeeksforgeeks+1
Which library in Python is commonly used for splitting datasets?
a. Matplotlib
b. scikit-learn
c. Seaborn
d. TensorFlow
Answer: b. scikit-learnplacementpreparation+2
Stratified splitting ensures what about the data?
a. Data types are consistent
b. Category proportions are maintained
c. Data is numeric
d. All missing values are handled
Answer: b. Category proportions are maintainedalooba+2
TimeSeriesSplit is best used for which data type?
a. Image data
b. Categorical data
c. Time-dependent data
d. Audio data
Answer: c. Time-dependent datascribd+2
Why is data randomization important before splitting?
a. Removes null values
b. Reduces model bias
c. Maximizes computation speed
d. Combines workloads
Answer: b. Reduces model biasbuiltin+1
What is the function used in Python’s scikit-learn for train-test split?
a. split_dataset()
b. do_split()
c. train_test_split()
d. set_split()
Answer: c. train_test_split()realpython+1
What is typically the role of a validation set?
a. Final model testing
b. Hyperparameter tuning
c. Data cleaning
d. Feature selection
Answer: b. Hyperparameter tuningencord+2
In top-down discretization, what process is used?
a. Merging
b. Splitting
c. Normalization
d. Encoding
Answer: b. Splittingsanfoundry+1
Which split approach is best for evaluating a model on sequential data?
a. K-Fold
b. Stratified Split
c. Time-based Split
d. Random Split
Answer: c. Time-based Splitgeeksforgeeks+2
What is the main disadvantage of only using train-test split?
a. High computational cost
b. Limited data for model training
c. Less reliable performance estimation
d. Requires labeled data
Answer: c. Less reliable performance estimationalooba+1
What parameter in train_test_split helps maintain class proportion?
a. balance
b. equalize
c. stratify
d. random_state
Answer: c. stratifyrealpython+2
Leave-One-Out cross-validation is best suited for which situation?
a. Large datasets
b. Small datasets
c. Imbalanced datasets
d. Categorical datasets
Answer: b. Small datasetsscribd+1
Data splitting in machine learning is crucial for:
a. Preventing data leakage
b. Model interpretation
c. Faster computation
d. Feature scaling
Answer: a. Preventing data leakageencord+1
What issue can occur if test data leaks into the training set?
a. Underfitting
b. Data poisoning
c. Overly optimistic model evaluation
d. Data redundancy
Answer: c. Overly optimistic model evaluationgeeksforgeeks+2
Which library function supports random state to ensure reproducibility in splits?
a. train_test_split
b. split_dataset
c. do_split
d. separate_data
Answer: a. train_test_splitrealpython+1
What does K in K-Fold Cross Validation denote?
a. Number of features
b. Number of classes
c. Number of folds/partitions
d. Model iterations
Answer: c. Number of folds/partitionsscribd+2
During model development, which split structure is the most robust for avoiding overfitting?
a. Train-Test only
b. Train-Validation-Test
c. Random split
d. Manual split
Answer: b. Train-Validation-Testencord+2
Which function is used for automatic splitting of names in R?
a. split
b. strsplit
c. autsplit
d. breaksplit
Answer: b. strsplitsanfoundry
Which splitting technique is required for predictive analytics on future time frames?
a. Random split
b. Sequential split
c. Custom split
d. Time-based split
Answer: d. Time-based splitalooba+1
What does the process of splitting in decision tree generation refer to?
a. Assigning unique values
b. Dividing data at a node
c. Pruning branches
d. Combining features
Answer: b. Dividing data at a nodesimplilearn+1
Which split method is not ideal for imbalanced classes?
a. Stratified split
b. Group split
c. Random split
d. TimeSeriesSplit
Answer: c. Random splitbuiltin+2
Which cross-validation method is best for time series forecasting?
a. K-Fold
b. TimeSeriesSplit
c. ShuffleSplit
d. GroupKFold
Answer: b. TimeSeriesSplitscribd
The main advantage of stratified sampling in data splitting is:
a. Faster computation
b. Balanced class distribution
c. Reduced storage
d. Automated labeling
Answer: b. Balanced class distributiongeeksforgeeks+2
The process of dividing data into training and test sets can help indicate:
a. Storage capacity
b. Model generalizability
c. Software version
d. Encryption strength
Answer: b. Model generalizabilityencord+2
Train-validation-test split generally uses what ratios?
a. 70:15:15
b. 80:10:10
c. 60:30:10
d. Variable
Answer: d. Variablegeeksforgeeks+1
What is the purpose of a validation set?
a. Optimizing model parameters
b. Encrypting data
c. Generating synthetic data
d. Transforming features
Answer: a. Optimizing model parametersalooba+1
K-Fold Cross Validation with K=5 has how many total training runs?
a. 5
b. 10
c. 1
d. K+1
Answer: a. 5scribd+2
What type of splits preserves the order of the data for sequential analysis?
a. Random splits
b. Stratified splits
c. Time-based splits
d. Uniform splits
Answer: c. Time-based splitsalooba+2
In K-Fold CV, each fold acts as what once?
a. Training set
b. Validation set
c. Test set
d. Test set only
Answer: c. Test setgeeksforgeeks+1
What Python keyword ensures reproducible train-test splits?
a. random_sample
b. random_state
c. seed
d. set_seed
Answer: b. random_staterealpython+1
Which class in scikit-learn offers cross-validation splitting strategies?
a. CrossValidator
b. model_selection
c. DataSplitter
d. TrainTestSplitter
Answer: b. model_selectionrealpython+1
Why is shuffle splitting used?
a. To balance dataset features
b. To create random partitions
c. To increase dataset size
d. To merge categories
Answer: b. To create random partitionsscribd+1
Which concept describes splitting at a node in a decision tree based on criteria?
a. Random split
b. Feature selection
c. Decision node split
d. Pruning
Answer: c. Decision node splitgeeksforgeeks+1
Which approach can best help assess model sensitivity to the data used?
a. Static train-test split
b. Multiple train-test splits
c. Single training set
d. Manual split only
Answer: b. Multiple train-test splitsencord+1
Which method divides data into k sequential groups for validation?
a. K-Fold Cross Validation
b. Stratified Sampling
c. Group Shuffle Split
d. Leave-One-Out CV
Answer: a. K-Fold Cross Validationalooba+2
When datasets are extremely skewed, which split is critical?
a. Manual split
b. Stratified split
c. Random split
d. Sequential split
Answer: b. Stratified splitbuiltin+2
What percentage is typically reserved for test data in standard splits?
a. 10%–20%
b. 30%–40%
c. 50%–60%
d. 80%–90%
Answer: a. 10%–20%realpython+1
Which metric is NOT typically directly impacted by data splitting?
a. Accuracy
b. Recall
c. F1 Score
d. Data volume
Answer: d. Data volumeencord+1
In Python, what argument in train_test_split() sets samples used for each run?
a. test_size
b. random_state
c. stratify
d. sampling
Answer: a. test_sizerealpython+1
What is the key difference between Shuffle Split and Stratified Split?
a. Randomization vs category preservation
b. Dataset size requirement
c. Feature transformation
d. Label encoding
Answer: a. Randomization vs category preservationbuiltin+2
What does model generalization mean?
a. Adapting to unseen data
b. Training faster
c. Increased complexity
d. Lower data volume
Answer: a. Adapting to unseen dataencord+1
Which split technique is most appropriate for multi-class classification problems?
a. Stratified split
b. Time series split
c. Shuffle split
d. Manual split
Answer: a. Stratified splitbuiltin+2
What method assesses model quality more accurately than single splits?
a. Random Split
b. K-Fold Cross Validation
c. Sequential Split
d. Manual Split
Answer: b. K-Fold Cross Validationgeeksforgeeks+2
Which split technique is preferred in anomaly detection tasks?
a. Manual split
b. Time-based split
c. Random split
d. Stratified split
Answer: d. Stratified splitbuiltin+1
How is the splitting ratio typically adjusted for very large datasets?
a. More data allocated to testing
b. More data allocated to validation
c. More data allocated to training
d. Fixed ratio
Answer: c. More data allocated to trainingalooba+2
What is the main goal of splitting data before model evaluation?
a. Secure the data
b. Simulate real-world predictive performance
c. Increase execution speed
d. Encode features
Answer: b. Simulate real-world predictive performancegeeksforgeeks+2

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