kaggle dataset

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

Finished Kaggle game has been nearly five months, today to summarize, for the autumn strokes to prepare.Title: The predictive model predicts whether the user will download the app after clicking on the mobile app ad based on the click Data provided by the organizer for more than 4 days and about 200 million times. Data set Features: The volume of data is large and there are 200 million of them. The data is unbalanced and th

(0.826) of the last use of naive Bayesian training. Now we start to make predictions for the test data, using the numTree=29,maxDepth=30 following parameters:val predictions = randomForestModel.predict(features).map { p => p.toInt }The results of the training to upload to the kaggle, the accuracy rate is 0.95929 , after my four parameter adjustment, the highest accuracy rate is 0.96586 , set the parameters are: numTree=55,maxDepth=30 , when I change

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

({' 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

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,

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

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, we can do a training model. Because this is a classification problem, you can use L1 SVM random forest classification algorithm, random forest is a very simple and practical classification model, adjustable variables are few. A very important variable

([' ID '], Axis=1) return train, labels, test, test_ids, Classes train, labels, test, Test_ids, classes = Encode (train, test) Train.head (1) OUT[2]: margin1 margin2 margin3 margin4 margin5 margin6 margin7 margin8 margin9 margin10 ... Texture55 texture56 texture57 texture58 texture59 Texture60 texture61 texture62 Texture63 texture64 0 0.007812 0.023438

Private voidDemonstratereadwritexmldocumentwithstreamreader () {//Create a DataSet with one table and both columns.DataSet Originaldataset =NewDataSet ("DataSet"); Originaldataset.namespace="NETFramework"; DataTable Table=NewDataTable ("Table"); DataColumn Idcolumn=NewDataColumn ("ID", Type.GetType ("System.Int32")); Idcolumn.autoincrement=true; DataColumn Itemcolumn=NewDataColumn ("Item"); Table. Column

Non-link access to the database-the queried Dataset is stored in Dataset ., Connection Mode dataset Private void Button_Click_1 (object sender, RoutedEventArgs e) {// access the database in non-link mode, // 1 create a connection object (connection string) using (SqlConnection conn = new SqlConnection (SQLHelper. connectionString) {// 2. create data adapter objec

Merges the specified dataset and its schema into the current dataset. namespaces: System.DataAssembly: System.Data (in System.Data.dll) C# Public void Merge ( DataSet DataSet ) ParametersDataSet Type: System.Data.. :: . DataSet The

Label: Private voidButton_click_1 (Objectsender, RoutedEventArgs e) { //accessing the database in a non-linked way,//1 Creating a Connection object (connection string) using(SqlConnection conn =NewSqlConnection (sqlhelper.connectionstring)) { //2. Create a data adapter object using(SqlDataAdapter SDA =NewSqlDataAdapter ("SELECT * from Student", conn)) { //3. Open the database connection (this step can actually be omitt

();StringBuilder sb = new StringBuilder ();while (reader. Read ()){Sb. Append ("Username:"). Append (reader. GetString (0)). Append ("\ n"). Append ("Password:"). Append (reader. GetString (1));}MessageBox.Show (sb.) ToString ());Second, the use of dataset data set to the SQLite database to insert data, but also directly affixed to code: DialogResult dlgresult= openfiledialog1.showdialog (); Open the file you want to importif (Openfiledialog1.filenam

Update to TensorFlow 1.4 I. Read input data 1. If the database size can be fully read in memory, use the simplest numpy arrays format: 1). Convert the Npy file into a TF. Tensor2). Using Dataset.from_tensor_slices ()Example: # Load The training data into two numpy arrays, for example using ' np.load () '. With Np.load ("/var/data/training_data.npy") as data: features = data["Features"] labels = data["Labels"] # assume that each row of features corresponds to the same row as ' labels '. Assert fe

Are you talking about typed dataset and untyped dataset? Typed Dataset is derived from dataset. It generates a dataset Based on the predefined data schema and imposes a strong type constraint on fields in the dataset. You can see

Label:One of the methods of SqlDataAdapter. Fill (DataSet DataSet, String DataTable) explains:Populates a DataSet with a DataTable name.Myda. Fill (ds, strtable);Strtable is not a variable, it is a virtual tableWhen you get a table of a database from a SQL statement and populate it with a dataset, the