Databricks-Certified-Professional-Data-Scientist Questions Bank

Classification : Build models to classify data into different categories credit scoring, tumor detection, image recognition Regression: Build models to predict continuous data, electricity load forecasting, algorithmic trading, drug discovery

In unsupervised learning we don't have a target variable as we did in

classification and regression.

Instead of telling the machine Predict Y for our data X, we're asking What can you

tell me about X?

Things we ask the machine to tell us about

X may be What are the six best groups we can make out of X? or What three

features occur together most frequently in X?

Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn

In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age and education. In other words, the fitted model should minimize the overall error between the linear model and the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters