Answer:
See explaination
Explanation:
public class PalindromeChange {
public static void main(String[] args) {
System.out.println("mom to non palindrom word is: "+changTONonPalindrom("mom"));
System.out.println("mom to non palindrom word is: "+changTONonPalindrom("aaabbaaa"));
}
private static String changTONonPalindrom(String str)
{
int mid=str.length()/2;
boolean found=false;
char character=' ';
int i;
for(i=mid-1;i>=0;i--)
{
character = str.charAt(i);
if(character!='a')
{
found=true;
break;
}
}
if(!found)
{
for(i=mid+1;i<str.length();i++)
{
character = str.charAt(i);
if(character!='a')
{
found=true;
break;
}
}
}
// This gives the character 'a'
int ascii = (int) character;
ascii-=1;
str = str.substring(0, i) + (char)ascii+ str.substring(i + 1);
return str;
}
}
Crowdsourcing<span> is the process of getting work or funding, usually online, from a crowd of people. The word is a combination of the words 'crowd' and 'outsourcing'. The idea is to take work and outsource it to a crowd of workers.</span>
Answer:
To get the same same results from all pots "amount of water should be same" for all pots.
Explanation:
As Carl want to measure and compare the amount of water that flows in pot in one minute from all all pots. He should keep the amount of water constant for all pots to get the desired results.
Answer:
b
Explanation:
First, we need to initialize the classifier.
Then, we are required to train the classifier.
The next step is to predict the target.
And finally, we need to evaluate the classifier model.
You will find different algorithms for solving the classification problem. Some of them are like decision tree classification etc.
However, you need to know how these classifier works. And its explained before:
You need to initialize the classifier at first.
All kinds of classifiers in the scikit-learn make use of the method fit(x,y) for fitting the model or the training for the given training set in level y.
The predict(x) returns the y which is the predicted label.And this is prediction.
For evaluating the classifier model- the score(x,y) gives back the certain score for a mentioned test data x as well as the test label y.
