Databricks Databricks-Certified-Professional-Data-Scientist - Databricks Certified Professional Data Scientist Exam

Regression is a tool which Companies may use this for things such as sales forecasts or forecasting manufacturing defects. Another creative example is predicting the probability of celebrity divorce.

We address two cases of the target variable. The first case occurs when the target variable can take only nominal values: true or false; reptile, fish: mammal, amphibian, plant, fungi. The second case of classification occurs when the target variable can take an infinite number of numeric values, such as 0.100, 42.001, 1000.743, .... This case is called regression.

(Change the explanation even it is correct)A normalizing constant is positive, and multiplying or dividing a series of values by a positive number does not affect which of them is the largest. Maximum likelihood estimation is concerned only with finding a maximum value, so normalizing constants can be ignored.

Explanation : Logistic regression

Pros: Computationally inexpensive, easy to implement, knowledge representation

easy to interpret

Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values

kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.

Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified,

labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.

Error calculation allows you to see how well a machine learning

method is performing.

One way of determining this performance is to calculate a numerical error This number is sometimes a percent,

however it can also be a score or distance. The goal is usually to minimize an error

percent or distance:

however th goal may be to minimize or maximize a score. Encog supports the

following error calculation methods.

Sum of Squares Error (ESS)

Root Mean Square Error (RMS)

Mean Square Error (MSE) (default)

SOM Error (Euclidean Distance Error)

RMSE measures error of a predicted numeric value, and so applies to contexts like

regression and some recommender system techniques,

which rely on predicting a numeric value. It is not relevant to classification

techniques

like logistic regression and Naive Bayes, which predict categorical values.

It also is not relevant to unsupervied techniques like clustering.

The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a

frequently used measure of the

differences between values predicted by a model or an estimator and the values

actually observed. Basically,

the RMSD represents the sample standard deviation of the differences between

predicted values and observed values.

These individual differences are called residuals when the calculations are

performed over the data sample that was used for estimation,

and are called prediction errors when computed out-of-sample. The RMSD serves

to aggregate the magnitudes

of the errors in predictions for various times into a single measure of predictive

power. RMSD is a good measure of accuracy,

but only to compare forecasting errors of different models for a particular variable

and not between variables, as it is scale-dependent.

Linear regression model generate continuous output variable.