Supervised and Unsupervised Learning

Supervised Learning

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into "regression" and "classification"problems. In a regression problem, we are trying to predict results within a continuousoutput, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.

Example:

Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.

We could turn this example into a classification problem by instead making our output about whether the house "sells for more or less than the asking price." Here we are classifying the houses based on price into two discrete categories.

Unsupervised Learning

Unsupervised learning, on the other hand, allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you. It’s not just about clustering. For example, associative memory is unsupervised learning.

Example:

Clustering: Take a collection of 1000 essays written on the US Economy, and find a way to automatically group these essays into a small number that are somehow similar or related by different variables, such as word frequency, sentence length, page count, and so on.

Associative: Suppose a doctor over years of experience forms associations in his mind between patient characteristics and illnesses that they have. If a new patient shows up then based on this patient’s characteristics such as symptoms, family medical history, physical attributes, mental outlook, etc the doctor associates possible illness or illnesses based on what the doctor has seen before with similar patients. This is not the same as rule based reasoning as in expert systems. In this case we would like to estimate a mapping function from patient characteristics into illnesses.

Hive DML ( Insert /Update/Delete)

How to check Hive Version : 

Command : ps -aux | grep -i "Hive"

and the find text "/lib/hive/lib/hive-service_VERSION_NUMBER" in the above command output. 

output  : /opt/cloudera/parcels/CDH-5.5.5-1.cdh5.5.5.p0.3/lib/hive/lib/hive-service-1.1.0-cdh5.5.5.jar

Here hive version is 1.1.0

 If your Hive version is 0.14 or greater then you can execute dml operation on hive tables.

However, below are the limitations :

1. 1  Table that you work with must be bucketed, declared as ORC format and has in it's table properties 'transactional'='true' (hive support ACID operations only for ORC format and transactional tables). An example of a proper table is like this:

 Below sql for your handy.

1.  create table testTableNew(id int ,name string ) clustered by (id) 

into 2 buckets stored as orc TBLPROPERTIES('transactional'='true');

2.  insert into table testTableNew values (1,'row1'),(2,'row2'),(3,'row3');

3.  update testTableNew set name = 'updateRow2' where id = 2;

4.  delete from testTableNew where id = 1;

5.  select * from testTableNew ;