Machine Learning Data Analyst Free Quiz

61. What is K-means clustering?

A clustering algorithm that partitions data into K clusters
A regression algorithm
A classification algorithm
A dimensionality reduction technique

62. What is the main objective of clustering in data analysis?

To group similar data points together
To predict continuous values
To identify outliers
To perform data visualization

63. What is the elbow method in K-means clustering?

A method to determine the optimal number of clusters
A way to measure clustering accuracy
A method to handle missing data
A technique for scaling features

64. What is an API in the context of data analysis?

A set of functions for interacting with a system
A visualization tool
A method for handling missing data
A machine learning model

65. What is feature engineering in machine learning?

The process of creating new features from raw data
The process of splitting data
A method to clean missing data
A way to scale data

66. In Python, what does the Seaborn library specialize in?

Data manipulation
Machine learning
Statistical data visualization
Text processing

67. What is the F1 score in classification problems?

The harmonic mean of precision and recall
The ratio of true positives to total positives
The difference between actual and predicted values
A metric for clustering quality

68. What is time series data?

Data that is grouped into clusters
Data collected or recorded at regular time intervals
Data that includes missing values
Data used in classification problems

69. What is a confusion matrix used for?

To calculate regression errors
To evaluate the performance of a classification model
To identify clusters
To visualize data

70. What is the purpose of cross-validation?

To split data into training and testing sets
To evaluate a model's performance on different subsets of the data
To identify outliers
To increase model accuracy

71. In SQL, what does the LIMIT clause do?

Limits the number of records returned
Sorts the records
Groups the records
Joins multiple tables

72. What is the recall metric in classification?

The ratio of true negatives to total predictions
The ratio of true positives to actual positives
The ratio of true positives to predicted positives
The harmonic mean of precision and recall

73. What is overfitting in machine learning?

A model performs well on training data but poorly on new data
A model that generalizes well
A model with high bias
A model that uses too few features

74. In Python, which function is used to remove missing data from a DataFrame?

drop_columns()
dropna()
drop_duplicates()
fillna()

75. What is a ROC curve in machine learning?

A plot of the true positive rate against the false positive rate
A method to evaluate regression models
AContinuing with questions 75–100:

75. What is a ROC curve in machine learning?

A plot of the true positive rate against the false positive rate
A method to evaluate regression models
A plot of precision against recall
A method to calculate accuracy

76. What is the difference between recall and precision?

Precision is the ability to retrieve all relevant instances; recall measures true positives
Recall is the ability to retrieve all relevant instances; precision measures true positives
Recall measures true positives; precision measures true negatives
Recall measures accuracy; precision measures errors

77. What is a bagging technique in machine learning?

An ensemble method that combines multiple models to improve accuracy
A method for reducing the number of features
A way to scale data
A method for clustering

78. What is gradient boosting in machine learning?

A way to reduce overfitting
An ensemble technique that builds models sequentially to minimize errors
A method for scaling features
A clustering algorithm

79. What is the purpose of one-hot encoding in machine learning?

To convert categorical variables into a binary format
To scale numerical features
To handle missing values
To remove duplicate data

80. What is the silhouette score in clustering?

A measure of how similar an object is to its own cluster compared to others
A method to calculate clustering accuracy
A method for scaling data
A clustering algorithm

81. What is regularization in machine learning?

A technique to reduce overfitting by penalizing large coefficients
A method to increase model complexity
A way to impute missing data
A method to scale features

82. What is the purpose of a validation set in machine learning?

To fine-tune the model and assess performance before testing
To train the model
To evaluate the final performance of the model
To visualize the data

83. What is bias in a machine learning model?

The error introduced by approximating a complex problem by a simplified model
The error due to noise in the data
The error that occurs during training
The error due to insufficient data

84. What is variance in a machine learning model?

The model's sensitivity to fluctuations in the training data
The error introduced by the model being too simple
The average of all errors
The spread of data points

85. What is a residual in linear regression?

The difference between the observed value and the predicted value
The coefficient of determination
The error between the predicted value and the actual value
The slope of the regression line

86. What is a support vector machine (SVM)?

A supervised learning model used for classification and regression
A clustering algorithm
A method for scaling features
A type of neural network

87. What is data leakage in machine learning?

When information from outside the training dataset is used to create the model
When the model overfits the data
When the dataset contains missing values
When the model is underfitting

88. What is logistic regression used for?

Predicting binary outcomes
Predicting continuous values
Clustering data points
Reducing the dimensionality of data

89. What is the goal of unsupervised learning?

To find patterns and relationships in data without labeled outcomes
To predict a specific label
To optimize a neural network
To split data into training and testing sets

90. What is the purpose of stratified sampling?

To ensure that each subgroup is proportionately represented
To select random samples from the population
To increase the sample size
To reduce bias in sampling

91. What is the purpose of the “train-test split” in machine learning?

To evaluate the performance of a model on unseen data
To reduce the number of features
To clean missing data
To perform feature scaling

92. What is ensemble learning in machine learning?

Combining multiple models to improve performance
A method for reducing the number of features
A clustering algorithm
A technique to handle missing data

93. What is the goal of dimensionality reduction?

To reduce the number of features in a dataset while preserving its information
To increase model complexity
To increase the number of data points
To scale data

94. What does the term “hyperparameter” refer to in machine learning?

Parameters that are set before the learning process begins
Parameters learned during training
Features in the dataset
Weights of the model

95. What is a decision boundary in classification?

A line that separates different classes in a classification problem
The maximum depth of a decision tree
The threshold for decision-making in regression
The final layer in a neural network

96. What is a learning curve in machine learning?

A graph showing the perform

ance of a model over time
A method for scaling data
A visualization of model predictions
A plot of precision and recall

97. What does it mean if a machine learning model has high variance?

The model performs well on training data but poorly on test data
The model performs consistently across all data
The model generalizes well
The model has too few features

98. What is a neural network in machine learning?

A model inspired by the structure of the human brain, used for classification and regression tasks
A clustering algorithm
A method for handling missing data
A method for scaling features

99. What is feature selection in machine learning?

The process of selecting the most relevant features for a model
A technique for scaling features
A method to add new features
A process for splitting data into training and testing sets

100. What is the bias-variance tradeoff in machine learning?

The balance between model complexity (variance) and model error (bias)
The difference between training and testing errors
The process of adjusting hyperparameters
The comparison between classification and regression