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Python Machine Learning Interview Questions MCQ Solutions for Students
Which scikit-learn function is typically used to determine the AUC-ROC score for a binary classification task?
a. compute_auc()
b. roc_curve()
c. auc()
d. roc_auc_score()
Option d – roc_auc_score()
In the context of K-fold cross-validation, what does the variable K signify?
a. Total number of output classes
b. Frequency of dataset shuffling
c. Number of models being created
d. Number of data splits used during validation
Option d – Number of data splits used during validation
Which scikit-learn function enables implementation of K-fold cross-validation?
a. kfold_validate()
b. cross_validate()
c. perform_cross_validation()
d. validate()
Option b – cross_validate()
Which of the following is most commonly used to assess regression model accuracy?
a. Mean Absolute Error (MAE)
b. R-squared
c. Mean Squared Error (MSE)
d. All of the above
Option d – All of the above
What is the purpose of the R-squared value in a regression model’s assessment?
a. It shows the amount of variability explained by the model
b. It gives the residual errors
c. It measures the average magnitude of prediction errors
d. It has no interpretive value
Option a – It shows the amount of variability explained by the model
Which scikit-learn function returns the R-squared score for a regression model?
a. evaluate_r_squared()
b. r_squared_score()
c. score()
d. compute_r_squared()
Option c – score()
What does the concept of overfitting describe in machine learning?
a. Model accuracy is high on training data but low on test data
b. The model performs similarly on both training and test sets
c. The model is too basic and misses important data patterns
d. Overfitting does not affect model performance
Option a – Model accuracy is high on training data but low on test data
In regression, which metric penalizes larger errors more than smaller ones?
a. Mean Absolute Error (MAE)
b. Root Mean Squared Error (RMSE)
c. Mean Squared Error (MSE)
d. R-squared
Option b – Root Mean Squared Error (RMSE)
What is the objective of using a train-test split while evaluating models?
a. Train on the entire dataset
b. Build a validation set for tuning parameters
c. Confirm model overfitting
d. Test how well the model performs on new data
Option d – Test how well the model performs on new data
Which function in scikit-learn is used to divide data into training and test sets?
a. split_data()
b. train_test_split()
c. create_train_test()
d. separate()
Option b – train_test_split()
What does underfitting mean in the context of evaluating model performance?
a. Model complexity is too high and it captures noise
b. Model achieves high accuracy on new data
c. Model fails to learn important patterns due to oversimplification
d. Underfitting has no impact on results
Option c – Model fails to learn important patterns due to oversimplification
Which command in Matplotlib allows you to specify the size of the figure?
a. plt.set_figure_size()
b. plt.figure_size()
c. plt.figsize()
d. plt.set_size()
Option c – plt.figsize()
How do you add a legend to a plot using Matplotlib?
a. plt.create_legend()
b. plt.legend()
c. plt.add_legend()
d. plt.set_legend()
Option b – plt.legend()
Which Python package is mainly used for tasks like tokenizing text, stemming, and tagging parts of speech?
a. NumPy
b. Pandas
c. NLTK
d. Scikit-learn
Option c – NLTK
What function in NLTK is designed to split text into words or sentences?
a. nltk.word_tokenize()
b. nltk.tokenize()
c. nltk.split()
d. nltk.text_tokenize()
Option a – nltk.word_tokenize()
Which module in NLTK helps reduce words to their root form by stemming?
a. nltk.stem
b. nltk.lemmatize
c. nltk.stemming
d. nltk.stemmer
Option a – nltk.stem
What method in NLTK is used to calculate how frequently each word appears in a text?
a. nltk.freq_dist()
b. nltk.FreqDist()
c. nltk.word_frequency()
d. nltk.frequency_distribution()
Option b – nltk.FreqDist()
How do you perform part-of-speech tagging on text using NLTK?
a. nltk.pos_tag()
b. nltk.tag()
c. nltk.postag()
d. nltk.tag_pos()
Option a – nltk.pos_tag()
Which NLTK module provides access to collections of text data and lexical resources?
a. nltk.corpus
b. nltk.resources
c. nltk.lexicon
d. nltk.data
Option a – nltk.corpus
Which tool in NLTK helps visualize text data, such as showing word dispersion and frequency?
a. nltk.Text()
b. nltk.draw()
c. nltk.plot()
d. nltk.visualize()
Option a – nltk.Text()
What NLTK method is used to convert words to their base or dictionary form (lemmatization)?
a. nltk.lemmatize()
b. nltk.lemmatizer()
c. nltk.lemmatization()
d. nltk.lemma()
Option a – nltk.lemmatize()
Which NLTK package offers resources for building and training language processing models?
a. nltk.models
b. nltk.classify
c. nltk.learn
d. nltk.ml
Option b – nltk.classify
How can similarity between two texts or documents be computed in NLTK?
a. nltk.Text().similarity()
b. nltk.similarity()
c. nltk.text_similarity()
d. nltk.compute_similarity()
Option a – nltk.Text().similarity()
What is the command to install the NLTK library via pip on Mac or Unix systems?
a. pip install nltk
b. sudo install nltk
c. brew install nltk
d. conda install nltk
Option a – pip install nltk
Which command launches the Python interpreter in the terminal on Mac or Unix?
a. python
b. python-shell
c. open-python
d. start-python
Option a – python
Which package manager is most commonly used for installing Python libraries like NLTK on Mac or Unix?
a. pip
b. conda
c. brew
d. apt
Option a – pip
What role does the NLTK download() function play after installation on a Windows system?
a. Installs necessary NLTK dependencies
b. Downloads the NLTK source files
c. Retrieves extra NLTK datasets and resources
d. Configures NLTK settings
Option c – Retrieves extra NLTK datasets and resources
What types of resources can users obtain through the NLTK downloader?
a. Only NLTK libraries
b. Documentation for NLTK
c. Supplementary datasets, corpora, and models
d. External Python packages
Option c – Supplementary datasets, corpora, and models
On a Windows machine, where is NLTK’s downloaded data typically saved by default?
a. C:\nltk_data
b. /usr/share/nltk_data
c. C:\Users<username>\AppData\Roaming\nltk_data
d. C:\Program Files\nltk
Option c – C:\Users
Which command or method helps verify NLTK’s installation and version details on Windows?
a. nltk version
b. pip show nltk
c. python -m nltk
d. nltk.about()
Option b – pip show nltk
How can NLTK be installed on a Windows system using Anaconda?
a. conda install -c anaconda nltk
b. pip install nltk-anaconda
c. anaconda install nltk
d. nltk install –conda
Option a – conda install -c anaconda nltk
Which technique is effective for encoding categorical variables in machine learning models?
a. One-Hot Encoding
b. Ordinal Encoding
c. Label Encoding
d. Frequency Encoding
Option a – One-Hot Encoding
For datasets with imbalanced classes, which evaluation metric prioritizes false positives more than false negatives?
a. Accuracy
b. Precision
c. Recall
d. F1-score
Option b – Precision
In Random Forest and similar ensemble techniques, what does the process of bootstrapping entail?
a. Combining predictions from multiple models
b. Using decision trees of varying depths
c. Generating multiple datasets by sampling with replacement
d. Sampling the entire dataset without replacement
Option c – Generating multiple datasets by sampling with replacement
What is the main purpose of applying regularization in machine learning models?
a. To make the model more complex
b. To prevent overfitting
c. To reduce the learning rate
d. To lower model bias
Option b – To prevent overfitting
In machine learning, what are hyperparameters?
a. Parameters learned during training
b. Settings tuned to improve model performance
c. Features used for making predictions
d. Parameters of the loss function
Option b – Settings tuned to improve model performance
Within gradient boosting algorithms like XGBoost, what does the learning rate parameter control?
a. The total number of trees in the model
b. The maximum depth of each tree
c. How much each tree contributes to the final prediction
d. The number of features considered
Option c – How much each tree contributes to the final prediction
How would you define machine learning?
a. A discipline focused on enabling computers to improve their performance without direct programming
b. A process for teaching computers to execute particular tasks
c. An approach that attempts to replicate human cognitive functions in machines
d. A method designed to automate tasks traditionally done manually
Option a – A discipline focused on enabling computers to improve their performance without direct programming
Which option does not represent a recognized category of machine learning?
a. Supervised learning
b. Unsupervised learning
c. Reinforcement learning
d. Pre-emptive learning
Option d – Pre-emptive learning
What is the main goal of using regularization in machine learning?
a. To discourage overly complex models and reduce the risk of overfitting
b. To enhance model accuracy by adding more input features
c. To make the model easier to understand
d. To decrease the time needed to train the model
Option a – To discourage overly complex models and reduce the risk of overfitting
Why is a validation set used during model training?
a. To measure how well the model performs on data not seen during training
b. To supply more data for training and enhance model performance
c. To evaluate the final model on entirely new test data
d. To choose the most effective model based on evaluation scores
Option d – To choose the most effective model based on evaluation scores
How do bagging and boosting differ?
a. Bagging creates a robust model by combining weak ones, while boosting strengthens learning by focusing on errors in multiple rounds
b. Bagging iteratively improves model performance, whereas boosting merges weak models into a stronger one
c. Both are the same in function but go by different names
d. Both are methods used in unsupervised learning
Option a – Bagging creates a robust model by combining weak ones, while boosting strengthens learning by focusing on errors in multiple rounds
Which of these is a widely used method in ensemble learning?
a. Decision tree
b. Linear regression
c. Support vector machine
d. Random forest
Option d – Random forest
Why is feature normalization applied in machine learning?
a. To speed up the training process
b. To help make the model easier to interpret
c. To scale input features to a common range
d. To eliminate missing data and outliers from the dataset
Option c – To scale input features to a common range
What does dimensionality reduction involve in machine learning?
a. Compressing high-dimensional datasets into a simpler, lower-dimensional form
b. Creating additional features from the current data
c. Filtering out anomalies from the dataset
d. Updating weight parameters in a neural network
Option a – Compressing high-dimensional datasets into a simpler, lower-dimensional form
Which of the following is frequently used for dimensionality reduction?
a. Logistic regression
b. K-nearest neighbors
c. Principal Component Analysis (PCA)
d. Ridge regression
Option c – Principal Component Analysis (PCA)
What is meant by the curse of dimensionality in machine learning?
a. The increase in model complexity and performance issues as more features are added
b. The challenge of picking the correct model for a specific task
c. The problem of having outliers in the dataset
d. The issue of imbalanced data in classification tasks
Option a – The increase in model complexity and performance issues as more features are added
Why is preprocessing data crucial in machine learning?
a. To prepare the dataset in an appropriate structure for training models
b. To make the model less complex
c. To make the model easier to understand
d. To raise model accuracy by improving feature quality
Option a – To prepare the dataset in an appropriate structure for training models
What is one common approach for handling missing data in a dataset?
a. Removing entries that contain missing values
b. Filling in missing entries using the average or median
c. Creating a separate category for missing values
d. Overlooking missing data during the model training process
Option b – Filling in missing entries using the average or median
Why is the softmax function used in neural networks?
a. To apply non-linear activation in the model
b. To transform output values into probability scores
c. To scale input values within a fixed range
d. To control how quickly the model learns
Option b – To transform output values into probability scores
Which metric is typically applied to assess regression models?
a. Accuracy
b. Mean Squared Error (MSE)
c. Precision
d. Recall
Option b – Mean Squared Error (MSE)
What is the role of L1 and L2 regularization in deep learning?
a. To discourage model complexity and reduce overfitting
b. To enhance model accuracy by adding more inputs
c. To simplify how the model can be interpreted
d. To make the model train faster
Option a – To discourage model complexity and reduce overfitting
How are unbalanced class distributions in classification usually addressed?
a. By expanding the dataset with altered copies
b. By removing samples from the majority class
c. By increasing the number of samples from the minority class
d. By grouping data into clusters
Option c – By increasing the number of samples from the minority class
What does dropout help achieve in deep neural networks?
a. It limits overfitting by randomly deactivating neurons during training
b. It reduces the total training duration
c. It adjusts feature values to a consistent scale
d. It eliminates extreme values in the dataset
Option a – It limits overfitting by randomly deactivating neurons during training
Which model is most suitable for producing sequences in deep learning tasks?
a. Decision tree algorithms
b. K-means clustering technique
c. Recurrent Neural Networks (RNNs)
d. Support vector machines (SVMs)
Option c – Recurrent Neural Networks (RNNs)
What is the goal of incorporating attention in neural architectures?
a. To focus on the most relevant parts of the input
b. To equalize the contribution of all features
c. To protect the model from overfitting
d. To compress input dimensions
Option a – To focus on the most relevant parts of the input
Which method is often chosen for sorting text into categories?
a. Linear regression
b. Naive Bayes classifier
c. Support vector machine (SVM)
d. Decision tree classifier
Option b – Naive Bayes classifier
What is the function of word embeddings in NLP?
a. To represent words as vectors that can be analyzed numerically
b. To eliminate common filler words from the input
c. To examine the grammatical makeup of a sentence
d. To create new terms from an existing word list
Option a – To represent words as vectors that can be analyzed numerically
What kind of model is typically applied for analyzing emotions in text?
a. Linear regression
b. Decision trees
c. Recurrent Neural Networks (RNNs)
d. K-means clustering
Option c – Recurrent Neural Networks (RNNs)
What issue do LSTM networks help solve in natural language tasks?
a. They detect key sections in the input
b. They address the problem of fading gradients in recurrent structures
c. They distribute weights evenly among inputs
d. They shrink the number of features used as input
Option b – They address the problem of fading gradients in recurrent structures
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