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CrushFTP

CrushFTP is a robust file transfer server that makes it easy to setup secure connections with your users 'Crush' comes from the built-in zip methods in CrushFTP They allow for downloading files in compressed formats in-stream or even automatically expanding zip files as they are received in-stream This is called ZipStreaming and can greatly

Candy Crush Saga Boosters Guide

After the 14th level you will be able to use the 'Coconut Wheel Booster' in Candy Crush Saga Use this booster by swapping it with another candy The coconut wheel has different results when using it in combination with other special candies or normal candies Just experiment with it and you'll see It's possible to move the coconut wheel Depending on which direction you move it it

Selecting good features

In my previous post I discussed univariate feature selection where each feature is evaluated independently with respect to the response variable Another popular approach is to utilize machine learning models for feature ranking Many machine learning models have either some inherent internal ranking of features or it is easy to generate the ranking from the structure of the model This applies to

Photoshop selection tools basics

Make a selection with a selection tool like the Quick Selection tool In the options bar click Select and Mask to open the Select and Mask workspace Go to the View menu on the right side of the workspace and choose one of the view options like Overlay for a more accurate view of your selection In Overlay view the selected area is clear and the non-selected area is translucent red by

How to Choose Which GPU a Game Uses on Windows 10

To select a game or traditional desktop application with an exe file select "Classic app" in the box click the "Browse" button and then locate the exe file on your system Most applications' exe files will probably be somewhere in one of your Program Files folders

ArcGIS Help 10 1

You can select features with your mouse pointer by clicking them one at a time or by dragging a box around them on the map Selecting features interactively Selecting features allows you to identify or work with a subset of features on your map You'll most likely work with selected features when you are querying exploring analyzing or editing data Applying a selection lets you specify

Functions and packages for feature selection in R

Feature Selection Using Wrapper Methods Example 1 – Traditional Methods Forward Selection – The algorithm starts with an empty model and keeps on adding the significant variables one by one to the model Backward Selection – In this technique we start with all the variables in the model and then keep on deleting the worst features one by one

Crusher

A crusher is a machine designed to reduce large rocks into smaller rocks gravel sand or rock dust Crushers may be used to reduce the size or change the form of waste materials so they can be more easily disposed of or recycled or to reduce the size of a solid mix of raw materials (as in rock ore) so that pieces of different composition can be differentiated Crushing is the process of

Feature Selection

Glaucoma Dataset 1 Boruta Boruta is a feature ranking and selection algorithm based on random forests algorithm The advantage with Boruta is that it clearly decides if a variable is important or not and helps to select variables that are statistically significant Besides you can adjust the strictness of the algorithm by adjusting the p values that defaults to 0 01 and the maxRuns

Select features for editing—ArcGIS Pro

Select overlapping features When you select overlapping features by clicking them a pop-up menu appears near the pointer enabling you to specify the feature you want to select You can cycle through the selection and highlight a feature in a map or scene

CrushFTP

Features CrushFTP is filled full of features Features you might not use right away but eventually as your business grows you'll wonder if it can handle a new task Usually the feature is already there just waiting for you to explore and use it! First time users will be up and running in no time and power users won't be disappointed with a

Photoshop selection tools basics

Make a selection with a selection tool like the Quick Selection tool In the options bar click Select and Mask to open the Select and Mask workspace Go to the View menu on the right side of the workspace and choose one of the view options like Overlay for a more accurate view of your selection In Overlay view the selected area is clear and the non-selected area is translucent red by

An Introduction to SQL Server FileStream

FILESTREAM Feature Summary With FILESTREAM the SQL Server team not only added a feature to handle unstructured data but also made sure that it smoothly integrates with many of the existing features of SQL Server FILESTREAM feature is available with all versions of SQL Server 2008 including SQL Server Express

The easiest way for getting feature names

Stack Overflow for Teams is a private secure spot for you and your coworkers to find and share information import pandas as pd from sklearn feature_selection import SelectKBest f_classif #Suppose we select 5 features with top 5 Fisher scores selector = SelectKBest(f_classif k = 5) #New dataframe with the selected features for later use in the classifier fit() method works too if you

Feature Selection Library

Feature Selection Library (FSLib 2018) is a widely applicable MATLAB library for feature selection (attribute or variable selection) capable of reducing the problem of high dimensionality to maximize the accuracy of data models the performance of automatic decision rules

The CrusheR

The CrusheR is a game created by TypicalType It inspired the The Crusher minigame found in Epic Minigames It has a UI design similar to the one found in Epic Minigames The player has to find holes in the ground to avoid a slowly descending ceiling Most of the maps contain obstacles (or parkour) that the player must go through in order to descend to lower levels Some maps have

Showing only selected features from attribute table on

One of the layers has around 5000 attribute entries based on a CSV file of species records All the features are of course shown on the map layer How can I select a specific class of features so that only they appear on a map so that for example a distribution map for a species can be generated I have tried Advanced Filter (Expression) so

The CrusheR

The CrusheR is a game created by TypicalType It inspired the The Crusher minigame found in Epic Minigames It has a UI design similar to the one found in Epic Minigames The player has to find holes in the ground to avoid a slowly descending ceiling Most of the maps contain obstacles (or parkour) that the player must go through in order to descend to lower levels Some maps have

sklearn feature_selection SelectKBest — scikit

sklearn feature_selection SelectKBest class sklearn feature_selection SelectKBest (score_func=function f_classif * k=10) [source] Select features according to the k highest scores Read more in the User Guide Parameters score_func callable Function taking two arrays X and y and returning a pair of arrays (scores pvalues) or a single array with scores

SQL SELECT Statement

SQL HOME SQL Intro SQL Syntax SQL Select SQL Select Distinct SQL Where SQL And Or Not SQL Order By SQL Insert Into SQL Null Values SQL Update SQL Delete SQL Select Top SQL Min and Max SQL Count Avg Sum SQL Like SQL Wildcards SQL In SQL Between SQL Aliases SQL Joins SQL Inner Join SQL Left Join SQL Right Join SQL Full Join SQL Self Join SQL

Functions and packages for feature selection in R

Feature Selection Using Wrapper Methods Example 1 – Traditional Methods Forward Selection – The algorithm starts with an empty model and keeps on adding the significant variables one by one to the model Backward Selection – In this technique we start with all the variables in the model and then keep on deleting the worst features one by one

tsfresh feature_selection package — tsfresh 0 16 1 dev17

The feature_selection module contains feature selection algorithms Those methods were suited to pick the best explaining features out of a massive amount of features Often the features have to be picked in situations where one has more features than samples Traditional feature selection methods can be not suitable for such situations which is why we propose a p-value based approach that inspects the

Using SQL Server 2012 T

In SQL Server 2012 the new system stored procedure sp_describe_first_set makes the work trivial sp_describe_first_result_set tsql = N'SELECT * FROM customers' Summary There are more T-SQL new features in the upcoming SQL Server 2012 Majority of them are designed to improve development efficiency and reduce development effort

6 Ways for Feature Selection

39 1s 3 [LightGBM] [Warning] Find whitespaces in feature_names replace with underlines 39 1s 4 [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf

6 Ways for Feature Selection

39 1s 3 [LightGBM] [Warning] Find whitespaces in feature_names replace with underlines 39 1s 4 [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf

6 Ways for Feature Selection

39 1s 3 [LightGBM] [Warning] Find whitespaces in feature_names replace with underlines 39 1s 4 [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf [LightGBM] [Warning] No further splits with positive gain best gain -inf

Selecting good features

In my previous post I discussed univariate feature selection where each feature is evaluated independently with respect to the response variable Another popular approach is to utilize machine learning models for feature ranking Many machine learning models have either some inherent internal ranking of features or it is easy to generate the ranking from the structure of the model This applies to

Candy Crush Saga Boosters Guide

After the 14th level you will be able to use the 'Coconut Wheel Booster' in Candy Crush Saga Use this booster by swapping it with another candy The coconut wheel has different results when using it in combination with other special candies or normal candies Just experiment with it and you'll see It's possible to move the coconut wheel Depending on which direction you move it it

ANOVA F

ANOVA F-value For Feature Selection 20 Dec 2017 If the features are categorical calculate a chi-square ($chi^{2}$) statistic between each feature and the target vector However if the features are quantitative compute the ANOVA F-value between each feature and the target vector The F-value scores examine if when we group the numerical feature by the target vector the means for each

ANOVA F

ANOVA F-value For Feature Selection 20 Dec 2017 If the features are categorical calculate a chi-square ($chi^{2}$) statistic between each feature and the target vector However if the features are quantitative compute the ANOVA F-value between each feature and the target vector The F-value scores examine if when we group the numerical feature by the target vector the means for each