greezybacon

class overloaded(object):
    """
    Simple (well, simple is relative) tool to allow for overloaded methods
    in Python. Overloaded methods are methods with the same name that
    receive different types of arguments. Statically typed languages such as
    C++ support overloaded methods at compile time; however, dynamically
    typed languages such as Python cannot support such methods, because no
    type hints can be specified in the function definition.

    This class operates as a decorator and allows for a cascaded design of

ibeex

A warning occurred (42 apples)
An error occurred

vincentarelbundock

vgoklani

from flask import Flask, render_template

app = Flask(__name__)

@app.route('/')
@app.route('/index')
def index(chartID = 'chart_ID', chart_type = 'bar', chart_height = 350):
	chart = {"renderTo": chartID, "type": chart_type, "height": chart_height,}
	series = [{"name": 'Label1', "data": [1,2,3]}, {"name": 'Label2', "data": [4, 5, 6]}]
	title = {"text": 'My Title'}

victorb

var myApp = angular.module('myApp', []);

myApp.directive('googleplace', function() {
    return {
        require: 'ngModel',
        link: function(scope, element, attrs, model) {
            var options = {
                types: [],
                componentRestrictions: {}
            };

mbbx6spp

Alternatives to the "Atlassian Stack"

Fuller stack alternatives (replaces at least two of the Atlassian stack)

• Phabricator

• Github

• Gitlab

• Taiga

• Redmine

fredbenenson

layout	title	description	tags
default	SQL Style Guide	A guide to writing clean, clear, and consistent SQL.	data process

Purpose

dyerrington

Overfitting in decision trees

Overfitting can be one problem that describes if your model no longer generalizes well.

Overfitting happens when any learning processing overly optimizes training set error at the cost test error. While it’s possible for training and testing to perform equality well in cross validation, it could be as the result of the data being very close in characteristics, which may not be a huge problem. In the case of decision tree’s they can learn a training set to a point of high granularity that makes them easily overfit. Allowing a decision tree to split to a granular degree, is the behavior of this model that makes it prone to learning every point extremely well — to the point of perfect classification — ie: overfitting.

I recommend the following steps to avoid overfitting:

• Use a test set that is not exactly like the training set, or different enough that error rates are going to be easy to see.

WillKoehrsen

# selenium for web driving
import selenium
from selenium import webdriver

# time for pausing between navigation
import time

# Datetime for recording time of submission
import datetime

vulcan25

from flask import Flask, g, jsonify
import werkzeug, os
from werkzeug.utils import secure_filename

import psycopg2
from psycopg2 import pool

def get_db():
    print ('GETTING CONN')
    if 'db' not in g: