"The best and average of both the algorithms is same which is O(nlog(n)), but the worst time complexity of QuickSort is O(n^2){a case where all the elements are sorted in opposite to the fashion/order you want} while the worst TC for merge sort remains the same O(nlog(n)).
and The SC for QS=O(logn) and MS=O(n)."
The_ A. - "The best and average of both the algorithms is same which is O(nlog(n)), but the worst time complexity of QuickSort is O(n^2){a case where all the elements are sorted in opposite to the fashion/order you want} while the worst TC for merge sort remains the same O(nlog(n)).
and The SC for QS=O(logn) and MS=O(n)."See full answer
Coding
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"Switching from a linear kernel to RBF / Gaussian kernel is likely to result in overfitting the model. It is a move that adds complexity to the mix, and if the data doesn't need that sort of complexity, it would result in overfitting. On the other hand, all the other three approaches would only try too reduce complexity in the process, thereby doesn't contribute to overfitting the model."
Sri V. - "Switching from a linear kernel to RBF / Gaussian kernel is likely to result in overfitting the model. It is a move that adds complexity to the mix, and if the data doesn't need that sort of complexity, it would result in overfitting. On the other hand, all the other three approaches would only try too reduce complexity in the process, thereby doesn't contribute to overfitting the model."See full answer
"AUC 0.5 equates to a random model, so when creating any machine learning model or statistical model, you ideally want your model to at least beat this random baseline."
Harsh S. - "AUC 0.5 equates to a random model, so when creating any machine learning model or statistical model, you ideally want your model to at least beat this random baseline."See full answer
"In details: setting k=1 in KNN makes the model fit very closely to the training data, capturing a lot of the data's noise and leading to a model that may not generalize well to unseen data. This results in a high-variance scenario."
Taha U. - "In details: setting k=1 in KNN makes the model fit very closely to the training data, capturing a lot of the data's noise and leading to a model that may not generalize well to unseen data. This results in a high-variance scenario."See full answer
"No ,MSE is suitable for only regression modes. Although the logistic regression in Its name has regression , but it is a classification problem so MSE is not suitable for classification models like logistic regression."
1036 loknadh R. - "No ,MSE is suitable for only regression modes. Although the logistic regression in Its name has regression , but it is a classification problem so MSE is not suitable for classification models like logistic regression."See full answer
"A/B testing is used when one wishes to only test minor front-end changes on the website.
Consider a scenario where an organization wishes to make significant changes to its existing page, such as wants to create an entirely new version of an existing web page URL and wants to analyze which one performs better. Obviously, the organization will not be willing to touch the existing web page design for comparison purposes.
In the above scenario, performing Split URL testing would be beneficial. T"
Sangeeta P. - "A/B testing is used when one wishes to only test minor front-end changes on the website.
Consider a scenario where an organization wishes to make significant changes to its existing page, such as wants to create an entirely new version of an existing web page URL and wants to analyze which one performs better. Obviously, the organization will not be willing to touch the existing web page design for comparison purposes.
In the above scenario, performing Split URL testing would be beneficial. T"See full answer
"It's mainly an experimentation technique for testing new features while the rest of the users are using the old product version of your product. In our case, we were using it for pre-release or announced release features for a specific group of users. We could at any point revert the experience or stop the feature and render the old product version of the product. Based on the success of the feature, we will then do a full rollout of the feature into production.
How does it work ?
Enable"
Karthik T. - "It's mainly an experimentation technique for testing new features while the rest of the users are using the old product version of your product. In our case, we were using it for pre-release or announced release features for a specific group of users. We could at any point revert the experience or stop the feature and render the old product version of the product. Based on the success of the feature, we will then do a full rollout of the feature into production.
How does it work ?
Enable"See full answer
"Marketing campaigns are run through different channels such as social media, emails, SEO, web advertising, events, etc. Let’s look at some of the overall success metrics at a broader level:
Total views for your campaign
Unique views for your campaign
Returning visitors for your campaign
Engagement for your campaign (If it’s a social media campaign, the marketer might be interested in knowing the number of users engaging with the campaign and the type of campaign positive/negative)
5"
Sangeeta P. - "Marketing campaigns are run through different channels such as social media, emails, SEO, web advertising, events, etc. Let’s look at some of the overall success metrics at a broader level:
Total views for your campaign
Unique views for your campaign
Returning visitors for your campaign
Engagement for your campaign (If it’s a social media campaign, the marketer might be interested in knowing the number of users engaging with the campaign and the type of campaign positive/negative)
5"See full answer
"If we are just getting a basic histogram on total heads flipped, this should work.
import random
import matplotlib.pyplot as plt
def coin_flip():
heads = []
for i in range(1000):
heads_counter = 0
for j in range (0, 20):
flip = random.randint(0, 10)
if flip <= 1:
heads_counter += 1
heads.append(heads_counter)
plt.hist(heads)
coin_flip()
`"
Eduardo F. - "If we are just getting a basic histogram on total heads flipped, this should work.
import random
import matplotlib.pyplot as plt
def coin_flip():
heads = []
for i in range(1000):
heads_counter = 0
for j in range (0, 20):
flip = random.randint(0, 10)
if flip <= 1:
heads_counter += 1
heads.append(heads_counter)
plt.hist(heads)
coin_flip()
`"See full answer
"def countuniqueoutfits(totalpants: int, uniquepants: int,
totalshirts: int, uniqueshirts: int,
totalhats: int, uniquehats: int) -> int:
"""
Number of unique outfits can simply be defined by
(uniquepantschoose1uniqueshirtschoose1uniquehatschoose_1)
(uniquepantschoose1*uniqueshirtschoose1) # Not wearing a hat
nchoosek is n
"""
res = (uniquepants*uniqueshirtsuniquehats) + (uniquepantsunique_shirts)
return res
print(countuniqueoutfits(2, 1, 1, 1, 3, 2))"
Sai R. - "def countuniqueoutfits(totalpants: int, uniquepants: int,
totalshirts: int, uniqueshirts: int,
totalhats: int, uniquehats: int) -> int:
"""
Number of unique outfits can simply be defined by
(uniquepantschoose1uniqueshirtschoose1uniquehatschoose_1)
(uniquepantschoose1*uniqueshirtschoose1) # Not wearing a hat
nchoosek is n
"""
res = (uniquepants*uniqueshirtsuniquehats) + (uniquepantsunique_shirts)
return res
print(countuniqueoutfits(2, 1, 1, 1, 3, 2))"See full answer