kaggle tutorials

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"Kaggle" using random forest classification algorithm to solve biologial response problem

Kaggle, get up.Kaggle games rely on machines for automatic processing, and machine learning is almost a must-have skill. Getting Started with Kaggle the machine learning skills required is not in-depth, just need to have a basic understanding of the common methods of machine learning, for example, for a problem, you can realize that it is a classification problem AH or regression problem ah, Why the machine

Kaggle actual combat record =>digit recognizer (July fully grasp the details and content)

Date:2016-07-11Today began to register the Kaggle, from digit recognizer began to learn,Since it is the first case for the entire process I am not yet aware of, first understand how the great God runs how to conceive and then imitate. Such a learning process may be more effective, and now see the top of the list with TensorFlow. Ps:tensorflow can be directly under the Linux environment, but it cannot be run in the Windows environment at this time (10,

Remember a failed Kaggle match (3): Where the failure is, greedy screening features, cross-validation, blending

):%0.4f"% (I+1,nfold, Aucscore) Meanauc+=aucsco Re #print "mean AUC:%0.4f"% (meanauc/nfold) return meanauc/nfolddef greedyfeatureadd (CLF, data, label, SCO Retype= "accuracy", goodfeatures=[], maxfeanum=100, eps=0.00005): scorehistorys=[] While Len (Scorehistorys) In fact, there are a lot of things to say, but this article on this side, after all, a 1000+ people's preaching will make people feel bored, in the future to participate in other competitions together to say it.http://blog.kaggle.com/2

Kaggle Practice 1--titanic

Recently has the plan through the practice Classics Kaggle case to exercise own actual combat ability, today has recorded oneself to do titanic the whole process of the practice. Background information: The Python code is as follows: #-*-Coding:utf-8-*-"" "Created on Fri Mar 12:00:46 2017 @author: Zch" "" Import pandas as PD from Sklearn.featur E_extraction Import Dictvectorizer from sklearn.ensemble import randomforestclassifier from xgboost import x

Kaggle Previous User classification problem

Kaggle Address Reference Model In fact, the key points of this project in the existence of a large number of discrete features, for the discrete dimension of the processing method is generally to each of the discrete dimension of each feature level like the SQL row to be converted into a dimension, the value of this dimension is only 0 or 1. But this is bound to lead to a burst of dimensions. This project is typical, with the merge function to connect

Kaggle Contest title--titanic:machine learning from Disaster

({' Female ': 1, ' Male ': 0}). astype (int) tf[' Fare '] = tf[' Fare '].map (lambda x : 0 if Np.isnan (x) Else int (x)). Astype (int) predicts = dt.predict (tf) ids = tf[' passengerid '].valuespredictions_file = Open (".. /submissions/dt_submission.csv "," WB ") Open_file_object = Csv.writer (predictions_file) Open_file_object.writerow ([" Passengerid "," survived "]) open_file_object.writerows (Zip (IDs, predicts)) Predictions_file.close ()The following is the importance of each node of the r

Kaggle on the classic discussion of predict Click-through rates on display ads, mainly on feature processing techniques

Links to Kaggle discussion area: HTTPS://WWW.KAGGLE.COM/C/CRITEO-DISPLAY-AD-CHALLENGE/FORUMS/T/10555/3-IDIOTS-SOLUTION-LIBFFM --------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------- Experience of feature processing in practical engineering: 1. Transforming infrequent features into a special tag. Conceptually,infrequent features should

Secret Kaggle Artifact Xgboost

computational speed and good model performance, which is the goal of this project for two points. The performance is fast because it has this design: parallelization:You can use all of the CPU cores to parallelize your achievements during training. Distributed Computing:Use distributed computing to train very large models. Out-of-core Computing:Out-of-core Computing can also be performed for very large datasets. Cache optimization of data structures and algorithms:better use of hardware. The fi

Using Theano to implement Kaggle handwriting recognition: Multilayer Perceptron

The previous blog introduced the use of the logistic regression to achieve kaggle handwriting recognition, this blog continues to introduce the use of multilayer perceptron to achieve handwriting recognition, and improve the accuracy rate. After I finished my last blog, I went to see some reptiles (not yet finished), so I had this blog after 40 days. Here, pandas is used to read the CSV file, the function is as follows. We used the first 8 parts of Tr

Download video tutorials from Chuanzhi blog | java video tutorials | net video tutorials | php video tutorials | webpage video tutorials

Video tutorial download summary | java video tutorial | net video tutorial | php video tutorial | webpage video tutorial download summary | java video tutorial | net video tutorial | php video tutorial | webpage video tutorial overview: this article collects and sorts out most of the video tutorials, documents, and source code of Zhizhi blogs. Welcome to favorites and reprint !!! Source: IT Tutorial: Download and summarize video

Tutorials | An introductory Python data analysis Library pandas

this is not the same Pandas knowledge you need to use in real-world data analysis. You can divide your study into two categories: Independent of data analysis, learning Pandas Library Learn to use Pandas in real-world data analysis For example, the difference between the two is similar to that of learning how to cut a twig in half, the latter is to chop some trees in the forest. Before we discuss this in more detail, let's take a look at both of these methods.Independent of da

Redis real-Combat tutorials, Redis cache tutorials, Redis message Publishing, subscriptions, Redis Message Queuing tutorials

(redisconnection connection)Throws DataAccessException {TODO auto-generated Method StubReturn Connection.lpush (Key.getbytes (), value.getbytes ());}});return l;} //Read message (no message is read in queue) key is the message channelpublic string Getfromqueue (final string key) {TODO auto-generated Method StubByte[] B = (byte[]) this.getredistemplate (). Execute (new rediscallbackPublic Object Doinredis (redisconnection connection)Throws DataAccessException {TODO auto-generated Method StubRetu

PHPCMS Two Development Tutorials (RPM), phpcms Two development Tutorials _php Tutorials

PHPCMS Two Development Tutorials (RPM), phpcms Two-time development tutorials Transferred from: http://www.cnblogs.com/semcoding/p/3347600.html Structural design of Phpcms V9 root directory|–API Structure file directory|–caches Cache file Directory|–configs System Configuration file directory|–caches_* System Cache Directory|–PHPCMS PHPCMS Framework Home Directory|–languages Framework Language Pack Direc

CSS Tutorials and jquery tutorials: WEBJX collection of foreign novice web design Tutorials

Article Introduction: with the popularity of CSS3, there are already a lot of Web sites made using CSS3, CSS3 offers a lot of design new technology and advanced features that make it easier to create sites. and jquery, as the hottest AJAX framework, is full of jquery on internet sites. In this article, you will share 29 new and useful jquery and CSS3 tutorials for novice web designers, hoping to With the popularity of CSS3, there are alrea

Reflection on learning video Tutorials: Reflection on video tutorials

Reflection on learning video Tutorials: Reflection on video tutorialsReflections on learning video tutorials I have read a lot of video tutorials, But I have spent a lot of time on them. But I also have some experiences. Let's write them here. Video tutorials are of different quality in general. Good

[Free resources] The latest stm32 series video tutorials and stm32 video tutorials

[Free resources] The latest stm32 series video tutorials and stm32 video tutorials MCU basics GPIO The STM32 series of tutorials were officially launched. We will be able to explain the series by David, the gold medal lecturer of the far-sighted startron. Through this series of tutorials, we can better understand embe

Kaggle Brush the game's sharp weapon, lr,lgbm,xgboost,keras__ machine learning

Brush the Race tool, thank the people who share. Summary Recently played a variety of games, here to share some general Model, a little change can be used Environment: Python 3.5.2 Xgboost:

Kaggle-Plankton Classification Competition First prize---translation (PART II)

Then the previous article Training 1) Validation We use the method of stratified sampling (stratified sampling) to separate the annotated datasets by 10% as a validation set (validation). Because the dataset is too small, our assessment on the

Kaggle Data Mining Competition preliminary--titanic <随机森林&特征重要性> __ Data Mining </随机森林&特征重要性>

The previous three posts have been a fairly complete feature engineering, analyzing string-type variables to get new variables, normalize numeric variables, get derived properties and make dimensional specifications. Now that we have a feature set,

Kaggle Code: Leaf classification Sklearn Classifier application

which Classifier is should I Choose? This is one of the most import questions to ask when approaching a machine learning problem. I find it easier to just test them all at once. Here's your favorite Scikit-learn algorithms applied to the leaf data.

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