10+ Nanobi Data & Analytics Interview Questions and Answers

Least Squares Method and Maximum Likelihood are both used to estimate parameters, but differ in their approach.

• Least Squares Method minimizes the sum of squared errors between the observed and predicted values.

• Maximum Likelihood estimates the parameters that maximize the likelihood of observing the given data.

• Least Squares Method assumes that the errors are normally distributed and independent.

• Maximum Likelihood does not make any assumptions about the distribution of errors.

• L...read more

To improve the performance of Linear Regression, you can consider feature engineering, regularization, and handling outliers.

• Perform feature engineering to create new features that capture important information.

• Apply regularization techniques like L1 or L2 regularization to prevent overfitting.

• Handle outliers by either removing them or using robust regression techniques.

• Check for multicollinearity among the independent variables and consider removing highly correlated variabl...read more

Metrics used to evaluate classification models

• Accuracy

• Precision

• Recall

• F1 Score

• ROC Curve

• Confusion Matrix

Overfitting in decision trees can be handled by pruning, reducing tree depth, increasing dataset size, and using ensemble methods.

• Prune the tree to remove unnecessary branches

• Reduce tree depth to prevent overfitting

• Increase dataset size to improve model generalization

• Use ensemble methods like Random Forest to reduce overfitting

• Underfitting can be handled by increasing tree depth, adding more features, and reducing regularization

• Regularization can be used to prevent overfittin...read more

Metrics used to evaluate Linear Regression

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• R-squared (R²)

• Adjusted R-squared (Adj R²)

• Mean Absolute Error (MAE)

• Residual Sum of Squares (RSS)

• Akaike Information Criterion (AIC)

• Bayesian Information Criterion (BIC)

Overfitting in Linear Regression can be handled by using regularization techniques.

• Regularization techniques like Ridge regression and Lasso regression can help in reducing overfitting.

• Cross-validation can be used to find the optimal regularization parameter.

• Feature selection and dimensionality reduction techniques can also help in reducing overfitting.

• Collecting more data can help in reducing overfitting by providing a more representative sample.

Assumptions in Linear Regression

• Linear relationship between independent and dependent variables

• Homoscedasticity (constant variance) of residuals

• Independence of residuals

• Normal distribution of residuals

• No multicollinearity among independent variables

Logistic Regression formula is used to model the probability of a certain event occurring.

• The formula is: P(Y=1) = e^(b0 + b1*X1 + b2*X2 + ... + bn*Xn) / (1 + e^(b0 + b1*X1 + b2*X2 + ... + bn*Xn))

• Y is the dependent variable and X1, X2, ..., Xn are the independent variables

• b0, b1, b2, ..., bn are the coefficients that need to be estimated

• The formula is used to predict the probability of a binary outcome, such as whether a customer will buy a product or not

• The formula is derive...read more

Type I error is rejecting a true null hypothesis, while Type II error is failing to reject a false null hypothesis.

• Type I error is also known as a false positive

• Type II error is also known as a false negative

• Type I error occurs when the significance level is set too high

• Type II error occurs when the significance level is set too low

• Examples: Type I error - Convicting an innocent person, Type II error - Failing to convict a guilty person

• Type I error is more serious in medical ...read more

Cost function measures the difference between predicted and actual values. Error function measures the average of cost function.

• Cost function is used to evaluate the performance of a machine learning model.

• It measures the difference between predicted and actual values.

• Error function is the average of cost function over the entire dataset.

• It is used to optimize the parameters of the model.

• Examples of cost functions are mean squared error, mean absolute error, and cross-entropy...read more