Mobile Application Architect Interview Questions and Answers

Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data to find patterns.

• Supervised learning requires a target variable to be predicted, while unsupervised learning does not.

• Supervised learning algorithms include regression, classification, and decision trees, while unsupervised learning algorithms include clustering and association.

• Examples of supervised learning include predicting housing prices based on features like location...read more

A random forecast model can be built using statistical methods and machine learning algorithms.

• Collect and clean the data to be used for the model

• Choose a suitable algorithm for the type of data and problem at hand

• Train the model using the data and algorithm

• Validate the model using a separate set of data

• Adjust the model parameters as needed

• Use the model to make predictions on new data

To avoid overfitting, use cross-validation, regularization, and increase data size.

• Use cross-validation to test the model on different data sets

• Regularize the model to penalize complex models

• Increase data size to improve the model's ability to generalize

• Use dropout to randomly remove nodes during training

• Use early stopping to prevent the model from overfitting

• Avoid using too many features or variables

• Use simpler models like decision trees or linear regression

• Use ensemble meth...read more

Univariate analysis deals with one variable, bivariate analysis deals with two variables, and multivariate analysis deals with more than two variables.

• Univariate analysis focuses on describing and analyzing a single variable, such as mean, median, mode, and standard deviation.

• Bivariate analysis examines the relationship between two variables, such as correlation and regression analysis.

• Multivariate analysis involves analyzing more than two variables, such as factor analysis a...read more