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TOP 100 MACHINE LEARNING INTERVIEW QUESTIONS – PART 2 (51-100)

Welcome to Part 2 of our Machine Learning Interview Q&A series!
In this segment (Q51–100), we’re diving deeper into some of the advanced and practical concepts in machine learning — from tackling overfitting and sequence learning to understanding classifiers, PCA vs ICA, and more.

Whether you’re preparing for interviews, brushing up on your fundamentals, or exploring real-world ML applications, these questions are designed to boost your confidence and clarity. Let’s continue leveling up your machine learning knowledge

51. What are the assumptions of Linear Regression?
Before applying linear regression, the following assumptions should be checked:

  1. A linear relationship exists between variables

  2. The data follows multivariate normal distribution

  3. Minimal multicollinearity among predictors

  4. No autocorrelation in residuals

  5. Constant variance of errors (homoscedasticity)

52. What is Variance Inflation Factor (VIF)?
VIF is a metric used to check multicollinearity in a regression model. It tells us how much the variance of a regression coefficient is inflated due to the correlation between predictors. A high VIF means the variable is highly collinear with others and may impact model stability.

53. When does a Linear Regression line stop adjusting?
The regression line stops adjusting when it reaches the point that maximizes the R-squared value. This means it has found the best fit line that explains the highest possible variance in the data.

54. Which ML algorithm is known as a Lazy Learner and why?
The K-Nearest Neighbors (KNN) algorithm is considered a lazy learner. It doesn’t learn any pattern during training. Instead, it stores the data and does all the computation during prediction by measuring distances between points.

55. Why does the beta coefficient change across subsets in regression?
If the beta values differ a lot when regression is done on different data subsets, it might indicate that the data isn’t uniform (heterogeneous). In such cases, it’s better to either segment the data and build separate models, or use a flexible model like decision trees.

56. How does training set size affect classifier choice?

57. Difference between Training Set and Test Set?

58. What is the difference between False Positive and False Negative?

59. What is Semi-Supervised Learning?
Semi-supervised learning uses a small amount of labeled data along with a large amount of unlabeled data. It combines the benefits of supervised and unsupervised learning and is useful when labeling data is expensive or time-consuming.

60. Where is Supervised Learning used in real-world businesses?
Supervised learning is widely used in:

  1. Medical diagnosis

  2. Fraud detection in finance

  3. Email spam filtering

  4. Analyzing customer sentiment on products/services

61. What is the difference between Inductive and Deductive Machine Learning?

62. What is a Random Forest in Machine Learning?
Random Forest is an ensemble learning algorithm used for both classification and regression. It builds multiple decision trees using random subsets of data and features. The final prediction is based on majority voting (for classification) or averaging (for regression) across all trees, which improves accuracy and reduces overfitting.

63. What is the Bias-Variance Trade-Off?

64. What is Pruning in Decision Trees and how is it done?
Pruning is a process of cutting down branches of a decision tree that add little value, to prevent overfitting.

65. How does the Reduced Error Pruning method work?
In reduced error pruning:

  1. Each node is checked to see if removing it improves model accuracy on validation data.

  2. If replacing a subtree with a leaf (having the most common class) improves or doesn’t worsen accuracy, pruning is done.

  3. This continues until no further improvement is seen.
    It works best when there’s plenty of validation data available.

66. What is Decision Tree Classification?
A decision tree is a model that splits data into subsets based on feature values. It builds a tree-like structure with nodes (decisions) and branches (outcomes). It works for both categorical and numerical data and helps in classifying input data into target categories.

67. What is Logistic Regression?
Logistic Regression is used when the dependent variable is binary (0 or 1). Unlike linear regression, it predicts probabilities using the logistic (sigmoid) function. Outputs above 0.5 are classified as 1, and those below as 0.

68. What are some techniques for Dimensionality Reduction?
You can reduce data dimensions by:

  1. Engineering new features or combining existing ones

  2. Removing highly correlated (collinear) features

  3. Using algorithms like PCA (Principal Component Analysis) or LDA (Linear Discriminant Analysis)

69. What is a Recommendation System?
Recommendation systems analyze user behavior and preferences to suggest products or content. They use:

70. Explain the K-Nearest Neighbors (KNN) Algorithm?
KNN is a simple supervised algorithm. When it gets new input, it compares it to stored data and picks the ‘K’ closest data points (neighbors). The input is classified based on the most common class among those neighbors. It’s based on distance and similarity.

71. How to Choose the Right Machine Learning Algorithm for Email Spam Filters?
To select an appropriate algorithm, consider these factors:

  1. The volume and type of data (categorical or continuous).

  2. The nature of the problem—classification, regression, clustering, or association.

  3. Whether the data is labeled (supervised), unlabeled (unsupervised), or a combination.

  4. The ultimate goal of the model.
    Analyzing these aspects helps in picking the most suitable algorithm for spam detection.

72. How is an Email Spam Filter Designed?
Here’s how you could build a spam filter:

  1. Feed the model with a large dataset of emails labeled as “spam” or “not spam.”

  2. A supervised learning approach is used to detect patterns in spam emails, like keywords—”lottery”, “win”, “free money”.

  3. Algorithms such as Decision Trees or SVMs can assess new emails using these learned patterns.

  4. If the likelihood of spam is high, the email is filtered out.

  5. The model with the best performance during testing is deployed.

73. Methods to Prevent Overfitting in Machine Learning Models
You can reduce overfitting through:

  1. Cross-validation: Splitting training data into multiple small sets for robust evaluation.

  2. Increasing training data: Helps the model generalize better.

  3. Feature selection: Removing less useful features can reduce noise.

  4. Early stopping: Stops training when the model stops improving.

  5. Regularization: Penalizes complexity to keep the model simple.

  6. Ensemble methods: Combines predictions from multiple models.

74. What is Selection Bias in Machine Learning?
Selection bias occurs when the data used for training doesn’t reflect the true population. Types include:

75. Types of Supervised Learning

  1. Regression: Predicts continuous values (e.g., salary, temperature). Algorithms include Linear and Logistic Regression.

  2. Classification: Categorizes data into labels (e.g., spam or not spam). Algorithms include Naive Bayes, Decision Trees, SVMs.

76. What is the Vanishing Gradient Problem?
This issue arises during training deep networks using gradient descent. The gradients shrink as they are backpropagated, especially in earlier layers, making training ineffective.

77. Solutions to the Vanishing Gradient Problem
Common techniques include:

78. Difference Between Data Mining and Machine Learning

Feature Data Mining Machine Learning
Purpose Extract patterns from large datasets Learn from data to make predictions
Human Involvement Often manual Mostly automated
Data Often unstructured Structured and preprocessed
Focus Data exploration Algorithm-based predictions

79. Types of Machine Learning Techniques

80. Applications of Unsupervised Learning

81. What is a Classifier?
A classifier is an algorithm that sorts data into categories. Common classifiers include:

82. What are Genetic Algorithms?
They are optimization techniques inspired by natural selection. Used in AI to find near-optimal solutions by evolving a population of candidate solutions.

83. Pattern Recognition Use Cases

84. What is a Perceptron?
A simple neural network model for binary classification.
Types:

85. What is Isotonic Regression?
It’s used to maintain a consistent order in predictions, often for calibrating probability outputs of classifiers.

86. Define Bayesian Networks
These are probabilistic graphical models that show relationships among variables using a Directed Acyclic Graph (DAG). Useful in modeling uncertainty, such as medical diagnoses.

87. Components of Bayesian Logic Programs

  1. Qualitative (Logical) Part: Structure via Bayesian clauses.

  2. Quantitative Part: Encodes the probabilities and relationships.

88. What is Incremental Learning in Ensembles?
It’s the capability of an algorithm to update itself using new data without retraining from scratch.

89. Elements of Relational Evaluation Techniques

90. Bias-Variance Decomposition

91. Sequential Supervised Learning Methods

92. What is Batch Statistical Learning?
Training data is processed in full or in mini-batches. When all data is processed together, it’s batch gradient descent; when done per data point, it’s stochastic.

93. Areas Facing Sequential Prediction Challenges

94. Types of Sequence Learning

95. What is Sequence Prediction?
Predicts the next item(s) in a sequence using past data. Used in:

96. What is PAC Learning?
PAC (Probably Approximately Correct) learning helps assess how likely a model is to perform well on unseen data, with a balance of accuracy and confidence.

97. PCA, KPCA, and ICA Explained

98. Three Stages in Model Building

  1. Building: Training the model

  2. Testing: Evaluating performance

  3. Deployment: Using it on real-world data

99. What is a Hypothesis in ML?
A hypothesis represents a function the model learns to map inputs to outputs. It’s a possible solution chosen by the learning algorithm.

100. Define Epoch, Entropy, Bias, and Variance

That wraps up Part 2 of our Machine Learning Interview Questions & Answers series!
You’ve now covered 100 curated questions — ranging from core theory to application-based insights. By now, you should have a stronger grip on how ML algorithms work, how to handle data-related challenges, and how to ace common interview discussions.

Stay tuned for more advanced topics and practical case studies. Until then, keep practicing, stay curious, and keep learning — the ML journey never really ends

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