kaggle datasets

, and finally calculates the classification Input: mnist DataSet or Fashion mnist dataset Output: Error rate and accuracy Mnist Data set: Take k=30, the verification set is 50, the accuracy rate is 1; Take k=30, the verification set is 500, the accuracy rate is 0.98; Take k=30, the validation set is 10,000, the accuracy rate is 0.84. Fashion mnist Data Set K=30, when the validation set is 10000, the t

: Network Disk DownloadContent Profile ...This book is intended for all readers interested in the practice and competition of machine learning and data mining, starting from scratch, based on the Python programming language, and gradually leading the reader to familiarize themselves with the most popular machine learning, data mining and natural language processing tools without involving a large number of mathematical models and complex programming knowledge. such as Scikitlearn, NLTK, Pandas,

, the use of the Out-of-core way, but really slow ah. Similar to the game 6,price numerical features or three-bit mapping into the category features and other categories of features together One-hot, the final features about 6 million, of course, the sparse matrix is stored, train file size 40G. Libliear seemingly do not support mini-batch, in order to save trouble have to find a large memory server dedicated to run lasso LR. As a result of the above filtering a lot of valuable information, ther

Kaggle is currently the best place for stragglers to use real data for machine learning practices, with real data and a large number of experienced contestants, as well as a good discussion sharing atmosphere. Tree-based boosting/ensemble method has achieved good results in actual combat, and Chen Tianchi provides high-quality algorithm implementation Xgboost also makes it easier and more efficient to build a solution based on this method, and many of

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 --------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------- Experience of feature processing in practical engineering: 1. Transforming infrequent features into a special tag. Conceptually,infrequent features should

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 validation set is affected by the noise, so we tested our validation set on other models on the leaderboard. 2) Training algorithm All models are based on the SGD optimization algorithm with Nesterov momentum. W

Because the structure of the satellite data (HDF data) is different from that of geotif, you must pay special attention to it when reading the data. Geotif data is generally a file that contains data in multiple bands. While while the modemis, a file contains multiple subdatasets. Gdal. Each subdataset contains multiple band data. In addition, the default compiled gdal does not include support for the modem_data. You need to download the source code for hdf4 and hdf5 separately, and then modify

You could ref:http://www.cvpapers.com/datasets.html I'll paste that contents in the followings: Participate in reproducible Detection PASCAL VOC, DataSet classification/detection competitions, segmentation competition, person Layout taster Competition Datasets LabelMe DataSet LabelMe is a web-based image annotation tool This allows researchers to label images and share T He annotations with the rest of the community. If you are using the database, we

The dataset is the central concept of ADO. Datasets can be treated as an in-memory database, which is a separate collection of data that is not dependent on the database. The so-called independence, that is, even if the data link is disconnected, or shut down the database, the dataset is still available, the dataset is internally XML to describe the data, because XML is a platform-independent, language-independent data Description language, and can de

Dataset Overview1.1 Dataset L is a memory resident structure that represents Relational Data L is a data view in XML format. It is a data relationship view. L in Visual Studio and. NET Framework, XML is the format used to store and transmit various data. Therefore, datasets are closely related to XML. 1.2 dataset Classification -Typed Dataset -Untyped Dataset 1.3 differences between a typed dataset and a non-Typed Dataset ArchitectureF

. NewRow ();//Add new line SqlCommandBuilder builder = new SqlCommandBuilder (adapter)//auto-Generate action command Adpter. Update (DataSet); } } } } Third, VS automatically generate strongly typed datasets (typed datasets)DataSet with XML Schema (XSD)It's essentially a dataset, but it's more of a schema definitionAdd-New Item-Data s

This article will simulate a data warehouse system with user data, product data, and order data. Create a multi-dimensional dataset based on the data structure and process it incrementally. Increment parties This article will simulate a data warehouse system with user data, product data, and order data. Create a multi-dimensional dataset based on the data structure and process it incrementally. Increment parties This article will simulate a data warehouse system with user data, product data,

This article will simulate a data warehouse system with user data, product data, and order data. Create a multi-dimensional dataset based on the data structure and process it incrementally. The incremental approach is to consider the growth of data in fact tables. Assuming that it will grow to several billion in the future, full processing will become unrealistic, therefore, the solution focuses on the incremental processing of multi-dimensional datasets