Brain, Bytes & Bias: ML Interview Questions You Can’t Miss!
- 2. Curated by Curated by Machine Learning is a subset of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. It involves the use of statistical techniques to enable systems to improve their performance on a specific task through experience. What is Machine Learning? Question 1
- 3. Curated by Curated by Supervised Learning: In supervised learning, the algorithm is trained on labeled data, where each input is associated with a corresponding output. It learns to map inputs to outputs and is used for tasks like classification and regression. Unsupervised Learning: Unsupervised learning deals with unlabeled data. The algorithm tries to find patterns or structure in the data, often through techniques like clustering and dimensionality reduction. Reinforcement Learning: Reinforcement learning involves an agent that learns to make sequential decisions by interacting with an environment. It receives rewards or penalties based on its actions and aims to maximize cumulative rewards. What are the different types of Machine Learning? Question 2 Machine Learning Reinforcement Learning Classification Regression Clustering Supervised Learning (Labeled) Unsupervised Learning (Unlabeled) Dimensionality Reduction
- 4. Curated by Curated by The bias-variance trade-off is a fundamental concept in Machine Learning. It refers to the trade-off between two sources of error: Achieving a good balance between bias and variance is essential for building models that generalize well to new, unseen data. Bias: High bias indicates that a model is too simplistic and unable to capture the underlying patterns in the data. This leads to underfitting, where the model performs poorly on both training and test data. Variance: High variance indicates that a model is too complex and sensitive to small fluctuations in the training data. This leads to overfitting, where the model performs well on the training data but poorly on the test data. What is the bias-variance trade-off in Machine Learning? Question 3
- 5. Curated by Curated by The curse of dimensionality refers to the problems and challenges that arise when working with high- dimensional data. As the number of features or dimensions increases, the amount of data required to effectively cover the feature space grows exponentially. This can lead to issues like increased computational complexity, overfitting, and difficulty in visualizing and interpreting the data. Feature engineering is the process of selecting, transforming, or creating new features from the raw data to improve the performance of machine learning models. It involves domain knowledge, creativity, andexperimentation to extract meaningful information from the data that can help the model make better predictions. What is the curse of dimensionality in Machine Learning? What is feature engineering in Machine Learning? Question Question 4 5
- 6. Curated by Curated by The difference is: What is the difference between classification and regression in Machine Learning? Question 6 Classification is a type of supervised learning where the goal is to predict the class or category of a data point. It's used when the output is discrete, such as classifying emails as spam or not spam. Regression is also a type of supervised learning but is used when the output is continuous. It predicts a numerical value, such as predicting the price of a house based on its features. Success Story Ajay Kumar From Gen To Success Story Ajay Kumar From Gen To
- 7. Curated by Curated by Explain the concept of overfitting in Machine Learning. Question 7 Overfitting occurs when a machine learning model learns the training data too well, including the noise and random fluctuations in the data. As a result, it performs very well on the training data but poorly on new, unseen data because it has essentially memorized the training data instead of learning the underlying patterns. It's a common problem that can be mitigated by techniques like cross-validation, regularization, and using more data.
- 8. Curated by Curated by What is cross-validation, and why is it important in Machine Learning? What is a confusion matrix in the context of classification? Question Question 8 9 Cross-validation is a technique used to assess the performance of a machine learning model by splitting the data into multiple subsets (folds). The model is trained and evaluated multiple times, with each fold serving as both the training and test set. Cross-validation provides a more reliable estimate of a model's performance and helps detect issues like overfitting or underfitting. A confusion matrix is a table that is used to evaluate the performance of a classification model. It shows the number of true positives, true negatives, false positives, and false negatives for a given set of predictions. It's a valuable tool for understanding the accuracy and error types of a classification model.
- 9. Curated by Curated by What are hyperparameters in Machine Learning? What is the bias-variance trade-off in Machine Learning? Question Question 10 11 Hyperparameters are parameters that are not learned from the data but are set prior to training a machine learning model. These parameters control aspects of the learning process, such as the learning rate in gradient descent or the depth of a decision tree. Tuning hyperparameters is crucial for optimizing model performance. The bias-variance trade-off refers to the balance that must be struck when training a machine learning model between making it simple enough to generalize well (low variance) and complex enough to capture underlying patterns (low bias). High bias results in underfitting, while high variance results in overfitting. Achieving the right balance is crucial for model performance.
- 10. Curated by Curated by What is the ROC curve, and how is it used in classification? What is regularization in Machine Learning, and why is it important? Question Question 12 13 The Receiver Operating Characteristic (ROC) curve is a graphical tool used to evaluate the performance of binary classification models. It plots the true positive rate (Sensitivity) against the false positive rate (1 - Specificity) at various thresholds for classification. The area under the ROC curve (AUC) is a common metric used to compare the performance of different models; a higher AUC indicates a better-performing model. Regularization is a technique used to prevent overfitting in machine learning models. It involves adding a penalty term to the loss function, discouraging the model from learning overly complex patterns. Common types of regularization include L1 regularization (Lasso), L2 regularization (Ridge), and dropout in neural networks.
- 11. Curated by Curated by What is the difference between precision and recall in classification? Question 14 Precision and recall are two important metrics used to evaluate the performance of a classification model. Precision and recall are often in tension with each other; increasing one may decrease the other. The F1-score is a metric that combines both precision and recall into a single value to balance this trade-off. Precision Recall Precision measures the ratio of true positive predictions to the total number of positive predictions made by the model. It answers the question, "Of all thepositive predictions made, how many were correct?" Recall (or Sensitivity) measures the ratio of true positive predictions to the total number of actual positive instances in the dataset. It answers the question, "Of all the actual positive instances, how many were correctly predicted by the model?" PREDICTED POSITIVE POSITIVE NEGATIVE ACTUAL NEGATIVE TRUE POSITIVES TRUE NEGATIVES FALSE POSITIVES FALSE NEGATIVES
- 12. Curated by Curated by What is the curse of dimensionality, and how does it affect machine learning algorithms? Question 15 The curse of dimensionality refers to the challenges that arise when dealing with high-dimensional data. As the number of features or dimensions in the data increases, the volume of the feature space grows exponentially. This can lead to problems such as increased computational complexity, data sparsity, and overfitting. Machine learning algorithms can struggle to find meaningful patterns in high-dimensional spaces without sufficient data. Courses Offered by Tutort Academy Full Stack Specialisation In Software Development Learn more Data Structures and Algorithms with System Design Learn more
- 13. Curated by Curated by What is the difference between bagging and boosting in ensemble learning? Question 16 Bagging (Bootstrap Aggregating): Bagging is an ensemble learning technique that involves training multiple base models independently on random subsets of the training data (with replacement). The final prediction is often obtained by averaging or voting among the predictions of these base models. Random Forest is a popular algorithm that uses bagging. Boosting: Boosting is another ensemble learning technique that focuses on training multiple base models sequentially, where each subsequent model is trained to correct the errors of the previous ones. Gradient Boosting and AdaBoost are examples of boosting algorithms.
- 14. Curated by Curated by What is the importance of data preprocessing in Machine Learning? What is the K-nearest neighbors (K-NN) algorithm, and how does it work? Question Question 17 18 Data preprocessing is a critical step in machine learning that involves cleaning, transforming, and preparing the data for model training. Proper data preprocessing can have a significant impact on model performance. It includes tasks such as handling missing values, scaling features, encoding categorical variables, and splitting data into training and testing sets. K-nearest neighbors (K-NN) is a simple supervised learning algorithm used for classification and regression tasks. In K-NN, the prediction for a new data point is based on the majority class (for classification) or the average of the K-nearest data points in the training set, where "K" is a user-defined parameter. The "nearest" data points are determined by a distance metric, typically Euclidean distance.
- 15. Curated by Curated by What is dimensionality reduction, and when is it useful in Machine Learning? Question 19 Dimensionality reduction is the process of reducing the number of features or dimensions in a dataset while preserving as much relevant information as possible. It is useful when dealing with high-dimensional data, as it can help mitigate the curse of dimensionality, reduce computational complexity, and improve model performance. Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are commonly used for dimensionality reduction. Success Story AjayKumar From To Success Story AjayKumar From To
- 16. Curated by Curated by What is the bias-variance trade-off in the context of model selection? Question 20 The bias-variance trade-off in model selection refers to the trade-off between model simplicity and model complexity. A model with high bias (simple) may underfit the data, while a model with high variance (complex) may overfit the data. Model selection involves finding the right balance between these two extremes to achieve good generalization performance.
- 17. Curated by Curated by What is a decision tree in Machine Learning? What is the bias-variance trade-off in the context of model evaluation? Question Question 21 22 A decision tree is a supervised machine learning algorithm used for both classification and regression tasks. It models decisions as a tree-like structure where each internal node represents a decision based on a feature, each branch represents an outcome of that decision, and each leaf node represents a final prediction. Decision trees are interpretable and can handle both categorical and numerical data. In the context of model evaluation, the bias-variance trade-off refers to the trade-off between underfitting and overfitting. A model with high bias (underfitting) has a simplistic representation that doesn't capture the underlying patterns in the data, leading to poor performance. On the other hand, a model with high variance (overfitting) fits the training data too closely and doesn't generalize well to new data. Model evaluation aims to strike a balance to achieve optimal predictive performance.
- 18. Curated by Curated by What is a neural network, and how does it work? Question 23 A neural network is a computational model inspired by the structure and function of the human brain. It consists of interconnected artificial neurons organized into layers, including an input layer, one or more hidden layers, and an output layer. Neural networks are used for a wide range of machine learning tasks, including image recognition, natural language processing, and reinforcement learning. They learn by adjusting the weights and biases of connections between neurons during training to minimize the error between predicted and actual outputs. Highest CTC Hiring Partners 350+ Career Transitions 1250+ 2.1CR Why Tutort Academy?
- 19. Curated by Curated by What is transfer learning in Machine Learning? Question 24 Transfer learning is a machine learning technique where a model trained on one task is adapted or fine-tuned for a different but related task. It leverages knowledge learned from one domain to improve performance in another domain, often saving time and resources. Pre-trained deep learning models, such as those based on Convolutional Neural Networks (CNNs) or Transformer architectures, are frequently used for transfer learning. Transfer Learning Task 1 Task 2 Knowledge transfer Data 1 Data 2 Model 1 Model 1 Head New Head Predictions 1 Predictions 2
- 20. Curated by Curated by What are some common challenges and limitations of Machine Learning? Question 25 Data Quality: ML models heavily rely on data quality, and noisy or biased data can lead to poor results. Interpretability: Many ML models, especially deep learning models, are considered "black boxes," making it challenging to interpret their decisions. Overfitting and Underfitting: Finding the right balance between model complexity and simplicity is a constant challenge. Computational Resources: Deep learning models can be computationally intensive, requiring powerful hardware for training. Computational Resources: Deep learning models can be computationally intensive, requiring powerful hardware for training. Ethical and Bias Concerns: ML models can inherit biases present in the training data, leading to fairness and ethical issues. Addressing these challenges is crucial for the responsible and effective application of machine learning in various domains.
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