Stolen from: https://stackoverflow.com/questions/33743978/spark-union-of-multiple-rdds
Outside of chaining unions this is the only way to do it for DataFrames.
from functools import reduce # For Python 3.x
from pyspark.sql import DataFrame
return reduce(DataFrame.unionAll, dfs)
unionAll(td2, td3, td4, td5, td6, td7, td8, td9, ...
Decided to go away and find the answers that would satisfy my question, and write them up here for anyone else wondering.
The .best_estimator_ attribute is an instance of the specified model type, which has the 'best' combination of given parameters from the param_grid. Whether or not this instance is useful depends on whether the refit parameter is set to ...
Let's assume that you are training a model whose performance depends on a set of hyperparameters. In the case of a neural network, these parameters may be for instance the learning rate or the number of training iterations.
Given a choice of hyperparameter values, you use the training set to train the model. But, how do you set the values for the ...
Both cross validation and bootstrapping are resampling methods.
bootstrap resamples with replacement (and usually produces new "surrogate" data sets with the same number of cases as the original data set). Due to the drawing with replacement, a bootstrapped data set may contain multiple instances of the same original cases, and may completely omit other ...
You actually would not want to resample your validation set after each epoch. If you did this your model would be trained on every single sample in your dataset and thus this will cause overfitting. You want to always split your data before the training process and then the algorithm should only be trained using the subset of the data for training.
The number of folds is usually determined by the number of instances contained in your dataset. For example, if you have 10 instances in your data, 10-fold cross-validation wouldn't make sense. $k$-fold cross validation is used for two main purposes, to tune hyper parameters and to better evaluate the performance of a model.
In both of these cases selecting ...
You do cross-validation when you want to do any of these two things:
Error Estimation of a Model
Model selection can come in different scenarios:
Selecting one algorithm vs others for a particular problem/dataset
Selecting hyper-parameters of a particular algorithm for a particular problem/dataset
(please notice that if you are both ...
Yes, you are correct that the dominant difference between the area under the curve of a receiver operator characteristic curve (ROC-AUC) and the area under the curve of a Precision-Recall curve (PR-AUC) lies in its tractability for unbalanced classes. They are very similar and have been shown to contain essentially the same information, however PR curves ...
From the Keras documentation, you can load the data into Train and Test sets like this
(X_train, y_train), (X_test, y_test) = mnist.load_data()
As for cross validation, you could follow this example from https://github.com/fchollet/keras/issues/1711
from sklearn.model_selection import StratifiedKFold
# load your data using this ...
The test set and cross validation set have different purposes. If you drop either one, you lose its benefits:
The cross validation set is used to help detect over-fitting and to assist in hyper-parameter search.
The test set is used to measure the performance of the model.
You cannot use the cross validation set to measure performance of your model ...
You should always do your evaluation of model performance on data that has not been over/undersampled. You can setup a pipeline with scikit-learn to perform your undersampling on the training set and then evaluate on the non-undersampled fold of data for each iteration as you described.
If k-fold cross-validation is used to optimize the model parameters, the training set is split into k parts. Training happens k times, each time leaving out a different part of the training set. Typically, the error of these k-models is averaged. This is done for each of the model parameters to be tested, and the model with the lowest error is chosen. The ...
Sometime, when the dataframes to combine do not have the same order of columns, it is better to df2.select(df1.columns) in order to ensure both df have the same column order before the union.
return functools.reduce(lambda df1,df2: df1.union(df2.select(df1.columns)), dfs)
df1 = spark.createDataFrame([[1,...
I am not sure if the validation set is balanced or not. You have a severe data imbalance problem. If you sample equally and randomly from each class to train your network, and then a percentage of what you sampled is used to validate your network , this means that you train and validate using balanced data set. In the testing you used imbalanced database. ...
Depends on how much CPU juice you are willing to afford for the same. Having a lower K means less variance and thus, more bias, while having a higher K means more variance and thus, and lower bias.
Also, one should keep in mind the computational costs for the different values. High K means more folds, thus higher computational time and vice versa. So, one ...
Which two accuracies I compare to see if the model is overfitting or not?
You should compare the training and test accuracies to identify over-fitting. A training accuracy that is subjectively far higher than test accuracy indicates over-fitting.
Here, "accuracy" is used in a broad sense, it can be replaced with F1, AUC, error (increase becomes decrease, ...
If you have an adequate number of samples and want to use all the data, then k-fold cross-validation is the way to go. Having ~1,500 seems like a lot but whether it is adequate for k-fold cross-validation also depends on the dimensionality of the data (number of attributes and number of attribute values). For example, if each observation has 100 attributes, ...
By default random forest picks up 2/3rd data for training and rest for testing for regression and almost 70% data for training and rest for testing during classification.By principle since it randomizes the variable selection during each tree split it's not prone to overfit unlike other models.However if you want to use CV using nfolds in sklearn you can ...
Yours is not an example of nested cross-validation.
Nested cross-validation is useful to figure out whether, say, a random forest or a SVM is better suited for your problem. Nested CV only outputs a score, it does not output a model like in your code.
This would be an example of nested cross validation:
from sklearn.datasets import load_boston
No. You don't select any of the k classifiers built during k-fold cross-validation. First of all, the purpose of cross-validation is not to come up with a predictive model, but to evaluate how accurately a predictive model will perform in practice. Second of all, for the sake of argument, let's say you were to use k-fold cross-validation with k=10 to find ...
Yep I figured it out. The answer is that by default GridSearchCV's last act is to expose the API of the estimator object you passed so that you can directly call things like .predict() or .score() on the GridSearchCV object itself. It does this by retraining the estimator against the best parameters it found during cross validation. If you want to skip this ...
No, he actually says the opposite:
One final note: I should say that in the machine learning as of this practice today, there are many people that will do that early thing that I talked about, and said that, you know...
Then he says (the "early thing" he talked about):
selecting your model as a test set and then using the same test set to report the ...
Nested cross validation estimates the generalization error of a model, so it is a good way to choose the best model from a list of candidate models and their associated parameter grids. The original post is close to doing nested CV: rather than doing a single train–test split, one should instead use a second cross-validation splitter. That is, one "nests" an ...
It seems that I missed the word "scoring". In fact, the extra 3 was related to the number of characters of 'mae'.
return cross_val_score(Ridge(alpha=float(alpha), random_state=2),
X_train, y_train, scoring='mae', cv=5).mean()
You can tune parameters only if you have already trained the model, otherwise there is nothing to tune.
However, i've also read that model selection shoud be done before tuning the parameters.
Before tuning you need to do some kind of pre-processing before tuning the parameters.
Usually your pipeline will consist of:
Get Data and Clean It.
Do some EDA ( ...
Your test score is incorrect. The ROC curve needs the probability scores from the model, not the class decisions. So replace
y_predicted = grid_clf.predict(X_test)
y_predicted = grid_clf.predict_proba(X_test)[:,1]
If you've got prior information then you should certainly not use simple mean in a split test. I assume you're trying to just predict which group will produce the greatest amount of revenue overall, by trying to emulate the underlying distribution.
Firstly, it's worth noting that any metrics you choose will actually reduce to mean in a pretty trivial way. ...
So, the underfitting means that you still have capacity for improving your learning while overfitting means that you have used a capacity more than needed for learning.
Green area is where testing error is rising i.e. you should continue providing capacity (either data points or model complexity) to gain better results. More green line goes, more flat it ...
A tuple of the form $(i_1, i_2, i_3, ... , i_n)$ gives you a network with $n$ hidden layers, where $i_k$ gives you the number of neurons in the $k$th hidden layer.
If you want three hidden layers with $10,30$ and $20$ neurons, your tuple would need to look like $(10,30,20)$.
$(100,1)$ would mean that the second hidden layer only has one neuron.
I think even this method is also called Ensemble Method.
How could I conclude that?
You might have heard about this algorithm named Random Forest,
what does it do? It take data randomly at row level and column level
builds different trees and takes an average of it. It is also
considered as one of the best algorithm for Prediction and
Classification. Can ...