Answer:The Mechanical Era
Created a machine that could add and subtract numbers. Dials were used to enter the numbers. ... Designed a machine called the Analytical Engine. The design had all the basic components of a modern day computer. In addition, it was designed to be programmable using punched cards.
Explanation:Hope this helped
== is an operator that returns a boolean if both operand are equal.
1101 * 10 is 11010
11000 + 10 is 11010
11010 == 11010
Thus the output would be True.
Answer:
import random
decisions = int(input("How many decisions: "))
for i in range(decisions):
number = random.randint(0, 1)
if number == 0:
print("heads")
else:
print("tails")
Explanation:
*The code is in Python.
import the random to be able to generate random numbers
Ask the user to enter the number of decisions
Create a for loop that iterates number of decisions times. For each round; generate a number between 0 and 1 using the randint() method. Check the number. If it is equal to 0, print "heads". Otherwise, print "tails"
The department store should consider using RFIDs (Radio Frequency Identification) for tracking inventory. Unlike the wireless barcodes, RFID uses radio waves to communicate with readers. One very common advantage of an RFID is the scanning range. Wireless barcodes, for instance, requires the reader to be close to the barcode before it can see it to scan it. However, RFID systems can scan a tag as long as it is within range. This is important because it reduces wastage of time on labor-intensive processes and increases task speed, convenience, and project turnover.
Many passive RFIDs use tags that are powered by electromagnetic energy. Such energy does not consume power.
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.
