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
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,
):%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
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 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
({' 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
Links to Kaggle discussion area: HTTPS://WWW.KAGGLE.COM/C/CRITEO-DISPLAY-AD-CHALLENGE/FORUMS/T/10555/3-IDIOTS-SOLUTION-LIBFFM
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Experience of feature processing in practical engineering:
1. Transforming infrequent features into a special tag. Conceptually,infrequent features should
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
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
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
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
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
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 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
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
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:
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
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,
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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