PatrickRudgeri

from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import StratifiedKFold, cross_validate
from sklearn.ensemble import VotingClassifier
import pandas as pd
from IPython.display import display


def cross_validation(estimator, model_name, X, y, n_splits=5, random_state=None):
    # definir metricas aqui (Se for métrica personalizada deverá utilizar `make_score`. Consultar docs do sklearn)

PatrickRudgeri

#include <iostream>
#include <chrono>
#include <cmath>

using namespace std;
using namespace std::chrono;

string func1(const string &nome) {
    int someVar = 1;
    for (int i = 0; i < 1e7; i++) {

PatrickRudgeri

#include <iostream>
#include <fstream>
#include <string>
#include <cstdlib>
#include <chrono>

using namespace std;
using namespace std::chrono;

int tamanhoArquivo(fstream& arq)

PatrickRudgeri

import numpy as np

def compute_log_loss(predicted, actual, eps=1e-14):
    """ Computes the logarithmic loss between predicted and
    actual when these are 1D arrays.
    :param predicted: The predicted probabilities as floats between 0-1
    :param actual: The actual binary labels. Either 0 or 1.
    :param eps (optional): log(0) is inf, so we need to offset our
    predicted values slightly by eps from 0 or 1.
    """