Sum = Sum - 2 * 1ll * st.Query(0,0,N-1,tin[lca]);
            Sum += C[lca];
            R[Q[i].idx] = Sum;
        }
    }
    for(auto r: R)cout << r << '\n';
}
int main(){
    cin.tie(0)->sync_with_stdio(0);
    memset(dp, -1, sizeof(dp));
    memset(tin, -1, sizeof(tin));
    memset(subt, 0, sizeof(subt));
    idx = 0;

    int t = 1;
    tC(t){
        Solve(ti);
    }
    return 0;
}

Sum of population ✅

Date - 30/12/2023
Company Name - Course i5
Role: Data Scientist

Q.   How can outlier values be treated?

A.  An outlier is an observation in a dataset that differs significantly from the rest of the data. This signifies that an outlier is much larger or smaller than the rest of the data.
Given are some of the methods of treating the outliers: Trimming or removing the outlier, Quantile based flooring and capping, Mean/Median imputation.


Q.   What is root cause analysis?

A.  A root cause is a component that contributed to a nonconformance and should be eradicated permanently through process improvement. The root cause is the most fundamental problem—the most fundamental reason—that puts in motion the entire cause-and-effect chain that leads to the problem (s). Root cause analysis (RCA) is a word that refers to a variety of approaches, tools, and procedures used to identify the root causes of problems. Some RCA approaches are more directed toward uncovering actual root causes than others, while others are more general problem-solving procedures, and yet others just provide support for the root cause analysis core activity.


Q.  What is bias and variance in Data Science?

A.  The model's simplifying assumptions simplify the target function, making it easier to estimate. Bias is the difference between the Predicted Value and the Expected Value in its most basic form. Variance refers to how much the target function's estimate will fluctuate as a result of varied training data. In contrast to bias, variance occurs when the model takes into account the data's fluctuations, or noise.


Q.  What is a confusion matrix?

A.   A confusion matrix is a method of summarising a classification algorithm's performance. Calculating a confusion matrix can help you understand what your classification model is getting right and where it is going wrong. This gives us the following: "True positive" for event values that were successfully predicted. "False positive" for event values that were mistakenly predicted. For successfully anticipated no-event values, "true negative" is used. "False negative" for no-event values that were mistakenly predicted.

————————————————————-

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Godrej Infotech Ltd is going to conduct a Recruitment Drive for B.Tech 2023 Freshers.

Drive Date- 4th Jan'2024
Location - Godrej Bhavan, Sec V, Saltlake, Kolkata
Position- Software Development Trainee

Criteria-
B.Tech 2023 Passout (CSE, IT & ECE)
No Year Gaps
70% Marks through out (10th, 12th, Diploma & Graduation)

I would request the Interested Candidates to share your CVs to below mail Id and contact the below given number.

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Role: Summer Analyst

Expected CTC: NA

Batch Eligible: 2024, 2025

Website: https://www.goldmansachs.com/careers/students/programs/india/summer-analyst-program.html

def subsetA(arr):
    arr.sort(reverse=True)
    subset_a = []

    sum_a = 0
    sum_b = 0

    for num in arr:
        if sum_a <= sum_b:
            subset_a.append(num)
            sum_a += num
        else:
            sum_b += num

    return sorted(subset_a)

Python 3✅
Array subset