ScikitLearn Interview Questions and Answers

What is Scikit-learn?

• Scikit-learn is a popular open-source machine learning library for Python. It provides simple and efficient tools for data analysis and modeling, including classification, regression, clustering, and dimensionality reduction.

What are the main features of Scikit-learn?

• Simple and efficient tools for data mining and data analysis.
• Built on NumPy, SciPy, and matplotlib, providing easy-to-use interfaces.
• Supports various supervised and unsupervised learning algorithms.
• Cross-validation, hyperparameter tuning, and model evaluation functionalities.
• Extensive documentation and community support.

What are the main differences between Scikit-learn and TensorFlow?

• Scikit-learn is focused on classical machine learning algorithms, while TensorFlow is more oriented towards deep learning and neural networks.
• Scikit-learn is easier to use for traditional algorithms, while TensorFlow requires more effort for deep learning models but offers better scalability and performance for large-scale neural networks.

What is a supervised learning algorithm in Scikit-learn?

• Supervised learning is a machine learning paradigm where the model is trained on labeled data. Examples include classification algorithms like Logistic Regression, SVM, and Decision Trees, as well as regression algorithms like Linear Regression.

What is an unsupervised learning algorithm in Scikit-learn?

• Unsupervised learning algorithms work with unlabeled data and aim to find hidden patterns or groupings. Examples include clustering algorithms like K-Means, DBSCAN, and dimensionality reduction techniques like PCA.

What is the purpose of cross-validation in Scikit-learn?

• Cross-validation helps assess the generalization ability of a model by splitting the dataset into multiple folds and training/testing the model on different subsets of the data.

What is GridSearchCV in Scikit-learn?

• GridSearchCV is used for hyperparameter tuning in Scikit-learn. It exhaustively searches a specified hyperparameter space and evaluates the model performance using cross-validation.

What is the purpose of feature scaling in Scikit-learn?

• Feature scaling is important because many machine learning algorithms, especially those that rely on distance metrics like k-NN and SVM, perform better when the data is scaled to a similar range or distribution. Scikit-learn provides scalers like StandardScaler and MinMaxScaler.

What are the differences between StandardScaler and MinMaxScaler?

• StandardScaler: Scales data by removing the mean and scaling to unit variance (standard normal distribution).
• MinMaxScaler: Scales data to a specified range, typically [0, 1], making the data more suitable for algorithms that require bounded input.

What is a confusion matrix?

• A confusion matrix is a table used to evaluate the performance of a classification algorithm. It compares the predicted labels to the true labels and shows the counts of true positives, false positives, true negatives, and false negatives.

What is the difference between precision, recall, and F1 score?

• Precision: The ratio of true positives to the total predicted positives (i.e., TP / (TP + FP)).
• Recall: The ratio of true positives to the total actual positives (i.e., TP / (TP + FN)).
• F1 Score: The harmonic mean of precision and recall, providing a balance between them (i.e., 2 * (precision * recall) / (precision + recall)).

What is a support vector machine (SVM) in Scikit-learn?

• A Support Vector Machine is a supervised machine learning algorithm used for classification tasks. It works by finding a hyperplane that best separates the data into different classes.

What is the difference between classification and regression in machine learning?

• Classification: Involves predicting a categorical label for input data.
• Regression: Involves predicting a continuous value for input data.

What is the purpose of the train_test_split function in Scikit-learn?

• The train_test_split function splits the dataset into training and testing sets, allowing you to evaluate the performance of a machine learning model on unseen data.

What is logistic regression in Scikit-learn?

• Logistic Regression is a linear model used for binary classification. It predicts the probability of an event occurring by applying the logistic function to the output of a linear combination of the input features.

What is the difference between a decision tree and a random forest?

• A decision tree is a simple model that splits data into branches based on feature values. A random forest is an ensemble method that combines multiple decision trees to improve prediction accuracy and reduce overfitting.

What is the purpose of the RandomForestClassifier in Scikit-learn?

• The RandomForestClassifier is an ensemble learning method that uses multiple decision trees to classify data and aggregates their predictions for better accuracy and robustness.

What is PCA (Principal Component Analysis)?

• PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional form by projecting the data onto a new set of axes called principal components.

What is k-means clustering in Scikit-learn?

• K-means clustering is an unsupervised learning algorithm used to partition data into k clusters based on similarity. It minimizes the variance within each cluster by adjusting the cluster centroids iteratively.

What is the difference between KMeans and DBSCAN clustering?

• KMeans: A centroid-based clustering algorithm that requires the number of clusters (k) to be specified in advance.
• DBSCAN: A density-based clustering algorithm that does not require specifying the number of clusters and can discover clusters of arbitrary shapes.

What is the purpose of the fit() method in Scikit-learn?

• The fit() method is used to train a machine learning model on the provided data. It learns the underlying patterns in the data and fits the model accordingly.

What is the difference between fit() and predict() methods in Scikit-learn?

• fit() is used to train the model using the provided training data, while predict() is used to make predictions on new, unseen data after the model has been trained.

What are hyperparameters in machine learning?

• Hyperparameters are parameters that are set before training the model and control the learning process. Examples include the learning rate, number of trees in a random forest, or the number of neighbors in k-NN.

What is the difference between L1 and L2 regularization?

• L1 regularization (Lasso) adds the absolute value of coefficients as a penalty to the loss function. It can result in sparse models with some feature coefficients set to zero.
• L2 regularization (Ridge) adds the squared value of coefficients as a penalty to the loss function. It encourages smaller coefficients but does not eliminate any features.

What is the use of the scoring parameter in Scikit-learn?

• The scoring parameter in Scikit-learn is used to specify the metric to evaluate the performance of a model, such as accuracy, precision, recall, or F1 score.

What is the purpose of the cross_val_score function in Scikit-learn?

• The cross_val_score function is used to evaluate a model by performing cross-validation, which splits the data into multiple folds and assesses the model's performance on each fold.

What is the purpose of the score() method in Scikit-learn?

• The score() method is used to evaluate the performance of a trained model on a dataset, typically returning a metric like accuracy for classification or R-squared for regression.

What is the Naive Bayes classifier?

• The Naive Bayes classifier is a probabilistic classifier based on Bayes' theorem, which assumes that features are independent given the class label. It is commonly used for text classification tasks.

What are the advantages of using RandomForest over a Decision Tree?

• Random Forest reduces the risk of overfitting compared to a single decision tree by averaging predictions from multiple trees.
• It handles both regression and classification problems efficiently and can manage large datasets with high-dimensional features.