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Data Science Interview Questions with Answers What’s the difference between random forest and gradient boosting? Random Forests builds each tree independently while Gradient Boosting builds one tree at a time. Random Forests combine results at the end of the process (by averaging or "majority rules") while Gradient Boosting combines results along the way. What happens to our linear regression model if we have three columns in our data: x, y, z  —  and z is a sum of x and y? We would not be able to perform the regression. Because z is linearly dependent on x and y so when performing the regression  would be a singular (not invertible) matrix. Which regularization techniques do you know? There are mainly two types of regularization, L1 Regularization (Lasso regularization) - Adds the sum of absolute values of the coefficients to the cost function. L2 Regularization (Ridge regularization) - Adds the sum of squares of coefficients to the cost function Here, Lambda determines the amount of regularization. How does L2 regularization look like in a linear model? L2 regularization adds a penalty term to our cost function which is equal to the sum of squares of models coefficients multiplied by a lambda hyperparameter. This technique makes sure that the coefficients are close to zero and is widely used in cases when we have a lot of features that might correlate with each other. What are the main parameters in the gradient boosting model? There are many parameters, but below are a few key defaults. learning_rate=0.1 (shrinkage). n_estimators=100 (number of trees). max_depth=3. min_samples_split=2. min_samples_leaf=1. subsample=1.0. Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

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10 commonly asked data science interview questions along with their answers 1️⃣ What is the difference between supervised and unsupervised learning? Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data. 2️⃣ Explain the bias-variance tradeoff in machine learning. The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance. 3️⃣ What is the Central Limit Theorem and why is it important in statistics? The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes. 4️⃣ Describe the process of feature selection and why it is important in machine learning. Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy. 5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them? Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data. 6️⃣ What is regularization and why is it used in machine learning? Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features. 7️⃣ How do you handle missing data in a dataset? Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly. 8️⃣ What is the difference between classification and regression in machine learning? Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome. 9️⃣ Explain the concept of cross-validation and why it is used. Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting. 🔟 What evaluation metrics would you use to evaluate a binary classification model? Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊