High - dimensional data

High training error and high test error

#include <bits/stdc++.h>
using namespace std;

class Solution {
public:
string clearStars(string A) {
string s = A;

priority_queue<char, vector<char>, greater<char>> pq;
vector<vector<int>> ind(26);
unordered_set<int> rs;

for (int i = 0; i < s.size(); ++i) {
if (s[i] == '*') {
rs.insert(i);
char ch = pq.top(); pq.pop();
pq.push(ch);

rs.insert(ind[ch - 'a'].back());
ind[ch - 'a'].pop_back();

if (ind[ch - 'a'].empty()) pq.pop();

continue;
}

if (ind[s[i] - 'a'].empty())
pq.push(s[i]);

ind[s[i] - 'a'].push_back(i);
}

string res = "";
for (int i = 0; i < s.size(); ++i) {
if (!rs.count(i)) {
res += s[i];
}
}

return res;
}
};


Clear stars
Start removal
C++

Python
Start removal

Company : BrowserStack
Role: Software Engineer(Backend)
Experience: 0- 1Years
Location: Mumbai /Remote

Apply now: https://browserstack.wd3.myworkdayjobs.com/en-US/External/job/Software-Engineer--Backend--Mumbai_JR102378


Telegram:- @allcoding1_official

1/2

4xy

4

30/84

150

Prior x Likelihood

C

Mode>median>mean

S2: 4xy
S7: 6
S12: 0
Q4: 1/2
S22: (2, 3)
S28: Target/Mean Encoding
S29: TimeSeriesSplit
S38: 15
S43: 2/5
S59: Mode > Median > Mean

ML - 2: The data has a Gaussian distribution
ML - 7: Updating prior beliefs with observed data using Bayes' theorem
ML - 12: The probability distribution over actions given states
ML - 17: Internal covariate shift
ML - 23: Boosting reduces bias, bagging reduces variance
ML - 24: Binary Cross-Entropy
S48: 30/84
S60: 150
S53: 2/3
S68: Prior × Likelihood


Amazon Machine Learning Summer School:

Exam Date: 3rd August 2025
Exam Duration: 60 mins

10:30 AM

Test format:
Section 1 - MCQ section - 20 questions
Section 2 - DSA type coding section - 2 questions

Answers available 👇👇👇
Telegram channel:- @allcoding1_official

M1: Encode sequential order

T1: Regularization

M16: Likelihood × Prior

ML2: SGD can escape local minima due to its noisy updates

ML3: Recursive Feature Elimination (RFE)

ML4: Gini Index

M4: It overfits the training data

M7: 0

M2: Strong negative linear relationship

S13: No real solution
S17: Local minimum
S23: Converges by Limit Comparison with 1/n²
S30: Collect recent user data and evaluate model drift
S31: Data leakage inflated model performance
S39: 6/216

Amazon ML School MCQ Answers 1:15 PM

Kill the enemy
C++
Amazon 1.15 PM


#include <vector>
#include <algorithm>

int solve(std::vector<int> &A, int B) {
long long m1 = 0, m2 = 0;

for (int val : A) {
if (val > m1) {
m2 = m1;
m1 = val;
} else if (val > m2) {
m2 = val;
}
}

long long b = B;
long long s = m1 + m2;

if (s == 0) {
return b > 0 ? -1 : 0;
}

long long k = b / s;
int ans = k * 2;
long long rem = b % s;

if (rem == 0) {
return ans;
} else if (rem <= m1) {
return ans + 1;
} else {
return ans + 2;
}
}

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