The answer is D. All of the above.
The computational complexity of K-NN increases as the size of the training data set increase and the algorithm gets significantly slower as the number of examples and independent variables increase.
Also, K-NN is a non-parametric machine learning algorithm and as such makes no assumption about the functional form of the problem at hand.
The algorithm works better with data of the same scale, hence normalizing the data prior to applying the algorithm is recommended.
Answer:
Result is statistically significant.
Step-by-step explanation:
Given that :
Chisquare statistic, χ² = 67.81
Critical value for the distribution, χ²critical = 3.84
α = 0.05
The Decison region :
If χ² statistic > Critical value ; Reject H0 ; this. Eans that result is statistically significant.
Therefore, since, 67.81 > 3.84 ; This means that the result is statistically significant at 0.05
6.4 to the nearest whole number is 6
1/4x-2 because you subtract the x from both sides, then you add 8 to both sides. Next you divide both sides by -4 to get y=1/4x-2
Answer:
16^¼=2
Step-by-step explanation:
16^¼=⁴√16
so, 2⁴=16
so it's 2,
<u>verification</u>
2×2×2×2=16
