kaggle learn

If the linear regression algorithm is like the Toyota Camry, then the gradient boost (GB) method is like the UH-60 Black Hawk helicopter. Xgboost algorithm as an implementation of GB is Kaggle machine learning competition victorious general. Unfortunately, many practitioners only use this algorithm as a black box (including the one I used to be). The purpose of this article is to introduce the principle of classical gradient lifting method intuitively

: 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

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

):%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

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. We set the coefficient of momentum to 0.9. Mos

approach to try again, accumulate experience. For a random forest how to get the importance of the face, you can look at the official documents of Scikit Learn Scikit-learn.org/stable/auto_examples/ensemble/plot_forest_ Importances.html#example-ensemble-plot-forest-importances-py Of course, after getting the important features, we have to remove the unimportant features to improve the model's training speed (the threshold can be adjusted slightly to

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

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

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reading is not enough, actual combat is the most important, so-called learning, such as in the "Python core programming 2" after class exercises/learning some small script/etc., here are several sites for the Python script instance study: (1). Code share list--Python (2). Python Code Library (3). https:// searchcode.com/ (4). GitHub best choice, more search some related projects, look at someone else's code, copy the wheel!! 4. Advanced entryThe Python advanced section of learning can ref

C ++ experts learn advice and learn with an open mind ~~ [Reprinted], advice and learn modestly 1. Take C ++ as a new language learning (it has nothing to do with C! True .); 2. Read Thinking In C ++ instead of C ++. 3. read "The C ++ Programming Language" and "Inside The C ++ Object Model". Do not read them because they are difficult and we are beginners; 4. Do

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0 reply content: basic syntax Coding skills and coding specifications Various functions Various PHP modules Learning a cms or secondary development Learn about PDO, ADO, data driver layer, and mysql Error Reporting Mechanism Object-oriented Use a framework to help developers Magic Design Mode Reflection Write tools and drivers. Write a small framework by yourself (to be honest, you don't have so much energy to write it. You need to understand a lot of

PHP Learning Notes (a) easy to learn php,php learn notes Target planning: With the first lesson, we can learn about the PHP environment. 1. Understanding of the environment: 2. Access method: 3. Modify the code and view it. 4. Use of variables 5. Code indentation to have a hierarchical relationship, and the best between the code to keep blank lines 6. Variable N

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:

Sharing Contest--a complete solution for R language. Machine learning courses on the Kaggle. Master machine learning. Introduce machine learning. Ensure that the machine learning resources available to the R language are viewed in the relevant Cran task view.Homework after class Get started with the statistics course. Learn Kaggle o