I'm running a model with differential evolution to minimize the cross validate score I set verbose=10 in order to get an idea about how long that will take then I'm getting the messages below:
But I don't got any idea what that means. Why is remaining time increasing?
Related
I've been working with the MLPClassifier for a while and I think I had a wrong interpretation of what the function is doing for the whole time and I think I got it right now, but I am not sure about that. So I will summarize my understanding and it would be great if you could add your thoughts on the right understanding.
So with the MLPClassifier we are building a neural network based on a training dataset. Setting early_stopping = True it is possible to use a validation dataset within the training process in order to check whether the network is working on a new set as well. If early_stopping = False, no validation within he process is done. After one has finished building, we can use the fitted model in order to predict on a third dataset if we wish to.
What I was thiking before is, that doing the whole training process a validation dataset is being taken aside anways with validating after every epoch.
I'm not sure if my question is understandable, but it would be great if you could help me to clear my thoughts.
The sklearn.neural_network.MLPClassifier uses (a variant of) Stochastic Gradient Descent (SGD) by default. Your question could be framed more generally as how SGD is used to optimize the parameter values in a supervised learning context. There is nothing specific to Multi-layer Perceptrons (MLP) here.
So with the MLPClassifier we are building a neural network based on a training dataset. Setting early_stopping = True it is possible to use a validation dataset within the training process
Correct, although it should be noted that this validation set is taken away from the original training set.
in order to check whether the network is working on a new set as well.
Not quite. The point of early stopping is to track the validation score during training and stop training as soon as the validation score stops improving significantly.
If early_stopping = False, no validation within [t]he process is done. After one has finished building, we can use the fitted model in order to predict on a third dataset if we wish to.
Correct.
What I was thiking before is, that doing the whole training process a validation dataset is being taken aside anways with validating after every epoch.
As you probably know by now, this is not so. The division of the learning process into epochs is somewhat arbitrary and has nothing to do with validation.
I am currently using machine learning model(written in Python3) to predict the product delivery date, but due to the nature of our business, customer always complain when the actual delivery date is later than the predicted delivery date. so I try to force the predicted date always later than the actual delivery date, but still as close to the actual date as possible. Can anyone advice me how to do this or any particular algorithm/methods that I can search for? Thank you in advance!
When you use a prediction from a machine learning model, you're saying that this date is the most probably date that It will be delivered.
Every prediction has an error associated to real values. You can describe these errors as a normal distribution of the probability to happen over time. Thus, you have 50% of chance to the real value is before your prediction and 50% of chance to be after your prediction.
You'll hardly predict the exact value.
But how can you overcome this?
You can use the root mean squared error (RMSE). This metric will tell you "how far" your predictions are from the real values. So, if you add 2 times RMSE to your predictions before sending to users, the probability of the real value appears after your prediction is < 5%.
I am using a Convolutional Neural Network and I am saving it and loading it via the model serializer class.
What I want to do is to be able to come back at a later time and continue training the model on new data provided to it.
What I am doing is I load it using
ComputationGraph net = ModelSerializer.restoreComputationGraph(modelFileName);
and then I give it the data like before with
net.train(dataSetIterator);
This seems to work, but it makes my accuracy really bad. It was about 89% before I did this, and, using the same data, it gets to be around 50% accurate after a few iterations (using the same data it just trained itself on, so if anything it should be getting stupidly more accurate right?).
Am I missing a step?
I think it'll be difficult to answer based on the information given, but I'll give you an example.
I had this exact problem. I had based my app on the GravesLSTMCharModellingExample (which is LSTM). I had saved my model after running for a couple of epochs (at which point it generated legible sentences), but when loading it, it produced garbage.
I thought everything was the same, but in the end it turned out I didn't initialize the CharacterIterator the same. When I fixed it, it worked as expected.
So to cut a long story short;
Check your values when initializing the auxiliary classes.
I am pretty new in ml so I am facing some difficulties realizing how could I use spark machine learning libraries with time series data that reflect to a sequence of events.
I have a table that contains this info:
StepN#, element_id, Session_id
Where step n# is the sequence in which each element appears, element_id is the element that has been clicked and session_id in which user session this happened.
It consists of multiple sessions and multiple element-sequence per session. i.e. one session will contain multiple lines of elements. Also each session would have the same starting and ending point.
My objective is to train a model that would use the element sequences observed to predict the next element that is most likely to be clicked. Meaning I need to predict the next event given the previous events.
(in other words I need to average users click behavior for a specific workflow so that the model will be able to predict the next most-relevant click based on the average)
From the papers and the examples I find online I understand that this makes sense when there is a single sequence of events that is meant to be used as an input for the training model.
In my case though, I have multiple sessions/instances of events (starting all at the same point) and I would like to train an averaging model. I find it a bit challenging though to understand how could that be approached using for example HMM in spark. Is there any practical example or tutorial that covers this case?
Thank you for spending the time to read my post. Any ideas would be appreciated!
This can also solve with frequent pattern mining. check this: https://spark.apache.org/docs/latest/mllib-frequent-pattern-mining.html
In this situation, you can find frequent items that occurred frequently together. In the first step you teach the model what is frequent, Then for prediction step, the model can see some events and can predict the most common events to this event
Consider a Machine Learning Algorithm which train from a training set, with the help of PAC learning model we get bounds on training sample size needed so the probability that error is limited(by epsilon) is bounded(by delta).
What does PAC learning model say about computational(time) complexity.
Suppose a Learning Algorithm is given more time(like more iterations) how the error and probability that error is limited changes
As an learning algorithm which takes one hour to train is of no practical use in financial prediction problems. I need how the performance changes as time given to algorithm changes both in terms of error bounds and what is the probability that error is bounded
The PAC model simply tells you how many pieces of data you need to get a certain level of error with some probability. This can be translated into the impact on the run time by looking at the actual machine learning algorithm your using.
For example, if your algorithm runs in time O(2^n), and the PAC model says you need 1000 examples to have a 95% chance of having .05 error and 10,000 example for .005 error, then you know you should expect a HUGE slowdown for the increased accuracy. Whereas the same PAC information for a O(log n) algorithm would probably lead you to go ahead and get the lower error.
On a side note, it sounds like you might be confused about how most supervised learning algorithms work:
Suppose a Learning Algorithm is given more time(like more iterations) how the error and probability that error is limited changes
In most cases you can't really just give the same algorithm more time and expect better results, unless you chance the parameters (e.g. learning rate) or increase the number of examples. Perhaps by 'iterations' you meant examples, in which case the impact of the number of examples on the probably and error rate can be found by manipulating the system of equations used for the PAC learning model; see the wiki article.