kaggle titanic

: 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

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

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

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

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

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 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

/* Wa becomes violent, and gets () cannot be used ()!!! Change to CIN. Getline. Read all the data to be used and bring it into a formula.Set two points on the Ball (x1, Y1), (X2, Y2 );Sum = r * ACOs (sin (X1) * sin (X2) + cos (X1) * Cos (X2) * Cos

Import TensorFlow as TF import numpy as NP import pandas as PD #数据预处理 def read_data (): Data=pd.read_csv (' train.csv ') #pandas read Data=data.fillna (0) #NAN fill in 0 datax=data[[' Sex ', ' age ', ' pclass ', ' sibsp ', ' parch ', ' Fare ', '

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

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.

Import Pylab as PL import NumPy as NP from sklearn.neighbors import kneighborsclassifier from Sklearn.metrics Import class Ification_report from sklearn.cross_validation import Train_test_split,stratifiedkfold,cross_val_score from

　Iv. selection of AlgorithmsThis step makes me very excited, finally talked about the algorithm, although no code, no formula. Because the tutorial does not want to go deep to explore the details of the algorithm, so focus on the application of the

Tags: CTO data sci Pictures RIP other Youdao some outCompetition DescriptionThe sinking of the RMS Titanic is one of the very infamous shipwrecks in history. On April, 1912, during she maiden voyage, the Titanic sank after colliding with a iceberg, killing 1502 out of 2224 PA Ssengers and crew. This sensational tragedy shocked the international community and LEDs to better safety regulations for ships.One o

purposes, models are created and evaluated based on the typical Kaggle 101 Titanic passenger dataset. Download the game page and related datasets: https://www.kaggle.com/c/titanic The sinking of the Titanic is a very famous haishu in history. I suddenly felt that I was not dealing with cold data, but using data mining