kaggle titanic

******************** Training took 7623.761000s!accuracy:96.18%　　In this data set, because the cluster of data distribution is better (if you understand this database, see its T-sne map can be seen.) Since the task is simple, it has been considered a toy dataset in the deep learning boundary, so KNN has a good effect. GBDT is a very good algorithm, in Kaggle and other big Data competition, the top tan Hua runner of the column can often see its figure

The ninth part of training special networkRemember the 70% training data we lost at the beginning? If we want to get a competitive score on the Kaggle leaderboard, that's not a good idea. In 70% of the data, there are quite a few features we don't see.So before we change the way we train only one model, we train several ad hoc networks, each of which can predict different sets of goals. We train a model to predict Left_eye_center and right_eye_center,

) , you can also follow one of the best courses onmachine learning course from Yaser Abu-mostafa. If you need more lucid explanation for the techniques, you can opt for Themachine learning course from Andrew Ng and follow The exercises on Python. tutorials (Individual guidance) On Scikit Learn Assignment: Try out this challenge on KaggleStep 7:practice, practice and practiceCongratulations, you made it!You are now having all the need in technical skills. It is a matter of practi

excellent Scikit-learn library APIs in deep learning as well, Nolearn wraps DECAF to make the life Easie R for you. It's a wrapper on top of DECAF and it's compatible (mostly) with Scikit-learn, which makes DECAF even more awesome. OverfeatOverfeat is a recent winner of Dogs vs Cats (Kaggle competition) which are written in C + + but it comes with a Python Wrappe R as well (along with Matlab and Lua). It uses GPU through Torch library so it's quite f

close to human-level accuracy overall, there could is subsets of the data where you perform poorly and wor King on those can boost production performance greatly.Finally, one might ask what is a good the defining human-level accuracy. For example, with the following image diagnosis setting, ignoring the cost of obtaining data, how should one pick the Criter IA for human-level accuracy? Typical human: 5% General Doctor: 1% Specialized Doctor: 0.8% Group of specialized doctor

Curve fittingMulti-collinearityDoes the virtual variable "cause multiple collinearity to have a big impact on machine learning?Teacher, my kaggle on the internet has failed to extract the data.The teacher focused on random forest and SVMand AdaBoost TensorFlow.Chen JieLink: http://pan.baidu.com/s/1i4PNJlr Password: fz7eDescribe the effect of multiple collinearity on machine learning?The data came up with a feature.Polar coordinatesEuclidean distance:

Recently registered Kaggle account, practice a simple KNN algorithm for handwritten digital recognition. The downloaded training and test text is stored using a CSV file, so you can pick up the CSV module here. CSV file The CSV full name (comma-separated values) is a format file, also known as a character split value. The records are split by newline characters, each record consists of fields, and the delimiters between fields are usuall

linear algebra and similar to numpy arrays.DecafDecaf is a recent deep learning library published by UC Berkeley, tested in the Imagenet Classification challenge, and its neural network implementation is very advanced (state of art).NolearnIf you want to use the excellent Scikit-learn Library API in deep learning, encapsulating the decaf Nolearn will make it easier for you to use it. It is the packaging for decaf, compatible with Scikit-learn (mostly), making decaf even more magical. (Thousand

explored by Breiman [inch] in 2001 that ensembles multiple decision trees where EAC H of them is curated by a random subset of instances and each node was selected from a random subset of features. Owing to its nature, it is called Random forests (RF) . RF has also theoretical and empirical proofs of endurance against over-fitting. Even AdaBoost shows weakness to over-fitting and outlier instances in the data, RF are more robust model against These caveats. (For more detail on RF, refer Tomy O

Recently did a lot of Kaggle machine learning contest, summed up in addition to an experience: Do feature enginering can go to the former 20, if you want to enter the first 10, then need ensemble method support, So recently, we have developed a thorough understanding of the following combinations of methods. Through learning to find the combination method is really tried, in the late stage of the competition, at the end of the cornered, may wish to tr

onmachine learning course from Yaser Abu-mostafa. If you need more lucid explanation for the techniques, you can opt for Themachine learning course from Andrew Ng and follow The exercises on Python. tutorials (Individual guidance) On Scikit Learn Assignment: Try out this challenge on KaggleStep 7:practice, practice and practiceCongratulations, you made it!You are now having all the need in technical skills. It is a matter of practice and what better place to practice than compe

Algorithmic/Data engineer essential Skills Basic knowledge Linear algebra Matrix theory Probability theory Stochastic process Graph theory Numerical analysis Optimization theory Machine learning Statistical learning methods Data mining Platform Linux Language Python Linux Shell Base Library NumPy Pandas Sklearn SciPy Matplotlib or Seaborn

Kaggle-titantic, From:https://www.youtube.com/watch?v=siepqqsplkaInstall Matplotlib:Conda Install MatplotlibInstall Scikit-learn:Conda Install Scikit-learnTrain_df.count () #查看缺失数据TRAIN_DF. Age.min () train_df. Age.max ()TRAIN_DF. Survived.value_counts ()TRAIN_DF. Sex.value_counts (). Plot (kind= ' bar)TRAIN_DF [(train_df[' Sex '] = = ' Male ') (train_df[' Pclass '] = = 1)] [' Survived '].value_counts (). Plot (kind= ' bar ')Data analysis with python

learning as well, Nolearn wraps DECAF to make the life Easie R for you. It's a wrapper on top of DECAF and it's compatible (mostly) with Scikit-learn, which makes DECAF even more awesome.OverfeatOverfeat is a recent winner of Dogs vs Cats (Kaggle competition) which are written in C + + but it comes with a Python Wrappe R as well (along with Matlab and Lua). It uses GPU through Torch library so it's quite fast. It also won the detection and localizati

Spark ML Model pipelines on distributed Deep neural Nets This notebook describes how to build machine learning pipelines with Spark ML for distributed versions of Keras deep ING models. As data set we use the Otto Product Classification challenge from Kaggle. The reason we chose this data are that it is small and very structured. This is way, we can focus the more on technical components rather than prepcrocessing. Also, users with slow hardware or w

also makes training more effective, so we choose to normalization the training data . At the same time in practice, normalization allows us to directly compare the weight of the formulas of each feature, and to see intuitively which features are more important. 2. Liblinear and Fm/xgboost actual effect comparison record In this round of transformation, the main actual attempt to liblinear the effects of the various models, but also the industry commonly used fm/xgboost for comparison test, the

Spark ML Model pipelines on distributed deep neural Nets This notebook describes what to build machine learning pipelines with Spark ML for distributed versions of Keras deep learn ING models. As data set we use the Otto Product Classification challenge from Kaggle. The reason we chose this data is, it is small and very structured. This is, we can focus on the technical components rather than prepcrocessing intricacies. Also, users with slow hardware

With LIGHTGBM and Xgboost respectively made the kaggle digit recognizer, try to use GRIDSEARCHCV tune the next parameter, mainly to Max_depth, Learning_rate, N_ Estimates and other parameters to debug, finally in 0.9747. Capacity is limited, and next we don't know how to further adjust the parameters. In addition, the Xgboost GRIDSEARCHCV will not be used, if there is a great God will, please inform. Paste the LIGHTGBM code: #!/usr/bin/python Import

Machine learning algorithms, the most discussed is a specific algorithm, such as decision TREE,KNN, in the actual work and Kaggle competition, Ensemble methods (combination method) The effect is often the best, of course, the need to consume training time will be elongated. The so-called ensemble methods, is to combine several machine learning algorithms together, or to combine the different parameters of an algorithm. Basically divided into the follo

interaction make for easier maintenance. One listener at a container level can handle multiple different event operations. This isn't an excuse for a monolithic function of titanic proportions. It is a easy-to-manage related events, often perform related functions or need to share data. If A parent container is listening then individual operations performed inside that container don ' t has to add or Remo ve listeners on their own. Operations like d