Databricks-Certified-Professional-Data-Scientist 無料問題集「Databricks Certified Professional Data Scientist」

（A）P(E2)/P(E1)

（B）P(E1+E2)/P(E1)

（C）P(E1)/P(E2)

（D）P(E2)/(P(E1+E2)

（A）K-means clustering

（B）Linear regression

（C）Logistic regression

（D）Semi Linear Regression

（E）Naive Bayesian classification

（A）Collaborative filtering

（B）Classification

（C）Clustering

（D）Regression

（E）Dimensionality reduction

（A）Support Vector Machines

（B）K-Means

（C）Naive Bayes

（D）K-Nearest Neighbors

（A）Include least mutual information with other selected features as a feature selection criterion

（B）Decrease the size of our training data

（C）Evaluate a model that only includes the top 100 words

（D）Include the number of times each of the words appears in the book in your model

（A）Neural networks

（B）Naive Bayes

（C）Linear Regression

（D）Random forests

（A）Hierarchical linear models

（B）Logistic Regression

（C）Linear Regression

（D）Maximum likelihood estimation

（E）Recommendation system

（A）Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.

（B）Ordinary Least Square can be used to estimates the parameters in linear model

（C）Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.

（D）In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.

（A）Web designing to represent best visuals of its results from algorithm.

（B）He should be creative

（C）He should possess database administrative skills.

（D）Should possess good programming skills

（E）Should be very good at mathematics and statistic

（A）Numerical

（B）Categorical

（C）Both 1 and 2 are correct

（D）None of the 1 and 2 are correct