data kaggle

, 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

(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

: 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

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

):%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 competition

factors, some groups such as women, children and the upper class are more likely to survive. In this question, we want you to analyze who is more likely to survive.Know that women and children have priority through prior knowledge of books, movies, etc. The same training data can be used to calculate the survival rate of women.#!/usr/bin/env python#coding:utf-8 "Created on November 25, 2014 @author:zhaohf" ' Import pandas as Pddf = Pd.read_csv (' ...

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

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

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

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,

networks did not make much difference in the performance of supervised learning. Possible reasons: When initializing the dense layer, it is possible that the weights of the dense layer is already in a reasonable range, making the convolution layer miss a lot of information (feature) during the pre-training phase. We found two ways to overcome this problem: temporarily keeping the pre-training layer constant for a while, just training the dense layer (random initialization). If you train only a

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: http://blog.csdn.net/han_xiaoyang/article/details/52665396Xgboost Official API:Http://xgboost.readthedocs.io/en/latest//python/python_api.htmlpreprocess[Python] View plain copy # Common preprocessing framework import pandas as PD import NumPy as NP import scipy as SP # file Read Def Read_csv_file (F, Logging

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. In [1]: Import NumPy as NP import pandas as PD import Seaborn as SNS import Matplotlib.pyplot as PLT def warn (*arg S, **kwargs): Pass import warnings Warnings.warn = Warn from sklearn.preprocessing impo

Do data science generally need to use similar xgboost, tensorflow, such as libraries, these libraries in win is not so good installation, but many people need them, how to do it, the simplest is to use Docker, not only a Linux virtual environment, You can also use Windows at the same time. It is actually a fairly easy to use software, this article does not teach too many commands, because I will not, will only speak a few basic commands. This article

　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

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 i

Reference Link: Https://www.tuicool.com/articles/QBZzquY The journey from Python rookie to Python Kaggler (Kaggle is a data modeling and data analysis competition platform) If you want to be a data scientist, or already a data scientist, you want to expand your skills, then

Several novice programmers won the Kaggle Predictive modeling contest after enrolling for a few days of "machine learning" courses on Coursera for free. The big data talent scare that the industry has made in it--McKinsey is the initiator--has raised expectations and demands for big data and advanced analytics talent, and dat