Answer:
Local Market or Primary Market.
Explanation:
The farmers sell their products to big traders in the bigger market in the urban areas. However, they also sell a part of their products to various village traders, who are located in the rural areas and can be found in local or primary markets. It's not so that they are not useful as well, as small farmers, in fact, sell the whole of their produce in the local or primary market as we know them by name. And the farmer gets a reasonable return there as well.
Memory Management
Processor Management
Device Management
File Management
Security
Answer:
Option b. ArrayList‹Integer› = new ArrayList‹Integer›(10) does not correctly declare an ArrayList.
<u>Explanation</u>:
ArrayList in Java dynamically stores elements in it. It also called as re- sizeable array as it expands in size when elements are added and decreases when an element is removed from it. This array list class also allows to use duplicate and null values.
The syntax for creating Array list is as follows
ArrayList<Type> obj = new ArrayList<Type>()
Type specifies the type of ArrayList like Integer, Character, Boolean etc, and obj is the object name. But in the option b ArrayList‹Integer› = new ArrayList‹Integer›(10) object name is missed and not specified.
Bayes’ Theorem provides a way that we can calculate the probability of a piece of data belonging to a given class, given our prior knowledge.
P(class|data) = (P(data|class) * P(class)) / P(data)
Where P(class|data) is the probability of class given the provided data.
Explanation:
- Naive Bayes is a classification algorithm for binary and multiclass classification problems.
- It is called Naive Bayes or idiot Bayes because the calculations of the probabilities for each class are simplified to make their calculations tractable.
This Naive Bayes tutorial is broken down into 5 parts:
Step 1: Separate By Class : Calculate the probability of data by the class they belong to, the so-called base rate. Separate our training data by class.
Step 2: Summarize Dataset : The two statistics we require from a given dataset are the mean and the standard deviation
The mean is the average value and can be calculated using :
mean = sum(x)/n * count(x)
Step 3: Summarize Data By Class : Statistics from our training dataset organized by class.
Step 4: Gaussian Probability Density Function : Probability or likelihood of observing a given real-value. One way we can do this is to assume that the values are drawn from a distribution, such as a bell curve or Gaussian distribution.
Step 5: Class Probabilities : The statistics calculated from our training data to calculate probabilities for new data. Probabilities are calculated separately for each class. This means that we first calculate the probability that a new piece of data belongs to the first class, then calculate the second class, on for all the classes.
