Looking for interview questions on Scikit-learn? Check out our list of 10 commonly asked questions and answers, covering topics such as key features, building a machine learning model, pipelines, cross-validation, overfitting, regularization, hyperparameters, evaluation metrics, and handling missing data in Scikit-learn.
Whether you’re a job seeker or a hiring manager, this guide can help you prepare for your next interview in machine learning.
Scikit-Learn Top Interview Questions
#1 What is Scikit-Learn?
Scikit-Learn is a Python machine learning library that provides tools for data analysis, modeling, and predictive analytics. It is built on top of NumPy, SciPy, and matplotlib.
#2 What are the key features of Scikit-Learn?
Scikit-Learn provides a wide range of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction. It also includes tools for data preprocessing, model selection, and model evaluation.
#3 What are the steps involved in building a machine learning model using Scikit-Learn?
The steps involved in building a machine learning model using Scikit-Learn are data loading, data preprocessing, feature selection, model selection, model training, model evaluation, and model deployment.
#4 What is a pipeline in Scikit-Learn?
A pipeline in Scikit-Learn is a sequence of data preprocessing and modeling steps that are executed in a specific order. Pipelines can be used to automate the process of building and evaluating machine learning models.
#5 What is cross-validation in Scikit-Learn?
Cross-validation in Scikit-Learn is a technique for evaluating the performance of a machine learning model. It involves splitting the data into multiple folds, training the model on each fold, and evaluating the performance on the remaining fold.
#6 What is overfitting in machine learning?
Overfitting is a common problem in machine learning where a model is too complex and learns the noise in the data instead of the underlying patterns. This leads to poor performance on new data.
#7 What is regularization in machine learning?
Regularization is a technique used to prevent overfitting by adding a penalty term to the model’s cost function. The penalty term encourages the model to have smaller weights and simpler decision boundaries.
#8 What is a hyperparameter in machine learning?
A hyperparameter in machine learning is a parameter that is set before training the model and controls the learning process. Examples of hyperparameters include the learning rate, regularization strength, and the number of hidden units in a neural network.
#9 What are the commonly used evaluation metrics in Scikit-Learn?
The commonly used evaluation metrics in Scikit-Learn include accuracy, precision, recall, F1-score, AUC-ROC, and mean squared error.
#10 How can you handle missing data in Scikit-Learn?
Scikit-Learn provides several methods for handling missing data, including dropping rows with missing values, imputing missing values using the mean or median, and using advanced imputation techniques such as K-nearest neighbors or matrix completion.