1601542080

# Are you dropping too many correlated features?

## Summary

Some commonly used correlation filtering methods have a tendency to drop more features than required. This problem is amplified as datasets become larger and with more pairwise correlations above a specified threshold. If we drop more variables than necessary, less information will be available potentially leading to suboptimal model performance. In this article, I will be demonstrating the shortcomings of current methods and proposing a possible solution.

## Example

Let’s look at an example of how current methods drop features that should have remained in the dataset. We will use the Boston Housing revised dataset and show examples in both R and Python.

#feature-engineering #analytics #data-science #machine-learning #correlation

1601542080

## Summary

Some commonly used correlation filtering methods have a tendency to drop more features than required. This problem is amplified as datasets become larger and with more pairwise correlations above a specified threshold. If we drop more variables than necessary, less information will be available potentially leading to suboptimal model performance. In this article, I will be demonstrating the shortcomings of current methods and proposing a possible solution.

## Example

Let’s look at an example of how current methods drop features that should have remained in the dataset. We will use the Boston Housing revised dataset and show examples in both R and Python.

#feature-engineering #analytics #data-science #machine-learning #correlation

1598661060

## Easy to use Correlation Feature Selection with Kydavra

Almost every person in data science or Machine Learning knows that one of the easiest ways to find relevant features for predicted value y is to find the features that are most correlated with y. However few (if not a mathematician) know that there are many types of correlation. In this article, I will shortly tell you about the 3 most popular types of Correlation and how you can easily apply them with Kydavra for feature selection.

Pearson correlation.

Pearson’s correlation coefficient in the covariance of two variables divided by the product of their standard deviations.

Figure 1. The formula to calculate the Pearson correlation between 2 features.

It’s valued between -1 and 1, negative values meaning inverse relation and positive, the reverse case. Often we just take the absolute value. So if the absolute value is above 0.5 the series can have (yes can have) a relation. However, we also set a vertical limit, 0.7 or 0.8, because if values are too correlated then possibly one series is derived from another (like age in months from age in years) or simply can drive our model to overfitting.

Using Kydavra PearsonCorrelationSelector.

Firstly you should install kydavra, if you don’t have it installed.

``````pip install kydavra
``````

Next, we should create an abject and apply it to the Hearth Disease UCI dataset.

``````from kydavra import PearsonCorrelationSelector

selector = PearsonCorrelationSelector()
selected_cols = selector.select(df, ‘target’)
``````

Applying the default setting of the selector on the Hearth Disease UCI Dataset will give us an empty list. This is because no feature has a correlation with the target feature higher than 0.5. That’s why we highly recommend you play around with parameters of the selector:

• **min_corr **(float, between 0 and 1, default=0.5) the minimal value of the correlation coefficient to be selected as an important feature.
• **max_corr **(float, between 0 and 1, default=0.5) the minimal value of the correlation coefficient to be selected as an important feature.
• **erase_corr **(boolean, default=False) if set to True then the algorithm will erase columns that are correlated between keeping just on, if False then it will keep all columns.

#feature-selection #machine-learning #correlation #sigmoid #artificial-intelligence

1600476300

## Getting Started With Feature Flags

### Introduction

As any developer can tell you, deploying any code carries technical risk. Software might crash or bugs might emerge. Deploying features carries additional user-related risk. Users might hate the new features or run into account management issues. With traditional deployments, all of this risk is absorbed at once.

Feature flags give developers the ability to separate these risks, dealing with one at a time. They can put the new code into production, see how that goes, and then turn on the features later once it’s clear the code is working as expected.

### What is a Feature Flag?

Simply put, a feature flag is a way to change a piece of software’s functionality without changing and re-deploying its code. Feature flags involve creating a powerful “if statement” surrounding some chunk of functionality in software (pockets of source code).

### The History of Feature Flags

Leading Web 2.0 companies with platforms and services that must maintain performance among high traffic levels led the way in regard to developing and popularizing new deployment techniques. Facebook, in particular, is known as a pioneer of feature flags and for releasing massive amounts of code at scale. While building its massive social network more than a decade ago, the company realized that its uptime and scale requirements could not be met with traditional site maintenance approaches. (A message saying the site was down while they deployed version 3.0 was not going to cut it).

Instead, Facebook just quietly rolled out a never-ending stream of updates without fanfare. Day to day, the site changed in subtle ways, adding and refining functionality. At the time, this was a mean feat of engineering. Other tech titans such as Uber and Netflix developed similar deployment capabilities as well.

The feature flag was philosophically fundamental to this development and set the standard for modern deployment maturity used by leading organizations everywhere today. Recently, feature flags have been used in tandem with continuous delivery (CD) tools to help forward-looking organizations bring features, rather than releases, to market more quickly.

#devops #continuous integration #ci/cd #continous delivery #feature flags #flags #feature branching #feature delivery

1598516160

## Effective Way To Replace Correlation With Predictive Power Score(PPS)

The strength of a linear relationship between two quantitative variables can be measured using Correlation. It is a statistical method that is very easy in order to calculate and to interpret. It is generally represented by ‘r’ known as the coefficient of correlation.

This is the reason why it is highly misused by professionals because correlation cannot be termed for causation. It is not necessary that if two variables have a correlation then one is dependent on the other and similarly if there is no correlation between two variables it is possible that they might have some relation. This is where PPS(Predictive Power Score) comes into the role.

Predictive Power Score works similar to the coefficient of correlation but has some additional functionalities like:

• It works on both Linear and Non-Linear Relationships
• Can be applied to both Numeric and Categorical columns
• It finds more patterns in the data.

In this article, we will explore how we can use the Predictive Power Score to replace correlation.

#### Implementation:

PPS is an open-source python library so we will install it like any other python library using pip install ppscore.

1. Importing required libraries

We will import ppscore along with pandas to load a dataset that we will work on.

`import ppscore as pps`

`import pandas as pd`

We will be using different datasets to explore different functionalities of PPS. We will first import an advertising dataset of an MNC which contains the target variable as ‘Sales’ and features like ‘TV’, ‘Radio’, etc.

`df = pd.read_csv(‘advertising.csv’)`

`df.head()`

1. Finding Relation using PPScore

We will use some basic functions defined in ppscore.

1. Finding the Relationship score

PP Score lies between 0(No Predictive Power) to 1(perfect predictive power), in this step we will find PPScore/Relationship between the target variable and the featured variable in the given dataset.

`pps.score(df, "Sales", "TV")`

#developers corner #coefficient of correlation #correlation analysis #dependency #heatmap #linear regression #replace correlation #visualization

1597487472

## Country State City Dropdown list in PHP MySQL PHP

Here, i will show you how to populate country state city in dropdown list in php mysql using ajax.

## Country State City Dropdown List in PHP using Ajax

You can use the below given steps to retrieve and display country, state and city in dropdown list in PHP MySQL database using jQuery ajax onchange:

• Step 1: Create Country State City Table
• Step 2: Insert Data Into Country State City Table
• Step 3: Create DB Connection PHP File
• Step 4: Create Html Form For Display Country, State and City Dropdown
• Step 5: Get States by Selected Country from MySQL Database in Dropdown List using PHP script
• Step 6: Get Cities by Selected State from MySQL Database in DropDown List using PHP script

https://www.tutsmake.com/country-state-city-database-in-mysql-php-ajax/

#country state city drop down list in php mysql #country state city database in mysql php #country state city drop down list using ajax in php #country state city drop down list using ajax in php demo #country state city drop down list using ajax php example #country state city drop down list in php mysql ajax