Answer:
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Explanation:
Answer:
Compare the predictions in terms of the predictors that were used, the magnitude of the difference between the two predictions, and the advantages and disadvantages of the two methods.
Our predictions for the two models were very simmilar. A difference of $32.78 (less than 1% of the total price of the car) is statistically insignificant in this case. Our binned model returned a whole number while the full model returned a more “accurate” price, but ultimately it is a wash. Both models had comparable accuracy, but the full regression seemed to be better trained. If we wanted to use the binned model I would suggest creating smaller bin ranges to prevent underfitting the model. However, when considering the the overall accuracy range and the car sale market both models would be
Explanation:
Answer:
The program to this question as follows:
Program:
targetValue = 0.3333 #defining variable targetValue and assign value
sensorReading = 0.0 #defining variable sensorReading and assign value
sensorReading = 1.0/3.0 #calculate value in sensorReading variable
Val=sensorReading - targetValue
#calculate the difference and store in Val variable
if (Val < 0.0001): #use of if block to check condition
print ("Equal") #print value
else: #else block
print ("Not equal") #print value
Output:
Equal
Explanation:
In the above Python program code, there are two variables "targetValue and sensorReading" is defined, in which targetValue store a value, that is "0.3333", and sensorReading holds a value, that is "0.0".
- In the next step, the "Val" variable is defined, that calculate the difference between both variable, that conditional statement is used.
- In if block, if the value is less then "0.0001", it will print value "Equal", otherwise, it will go to the else block, that will print "Not equal".
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