Let assume I have a data structured as follows:
ID, Feature 1, feature 2, feature 3, outcome
The historical data is present for all features.
but the prediction need to be done at a stage where only ID, Feature 1, feature 2 are available.
how can we build a kind of prediction (predict the outcome) by taking advantage of feature 3 from historical data?
Is this assumption correct? or is there any strategies to be adopted.
Thank you in advance
If you have sufficient data, try comparing a model on all 3 features vs only 1 & 2 by predicting entirely within that set (using train, test, score per example). From there you could see where -- and how -- the 3rd feature gives you an edge. You could then use that information to calibrate the 2-features predictor and apply it to the actual scoring set.
Related
I want to train an MLP that takes in two nba teams A and B and classifies one as a winner and the other as a loser (probably binary classification 0 for loser 1 for winner) as well as a predictor that assigns a probability that team A wins vs team B. I'm having trouble figuring out how the feature vector should look though and would like some advice. My ideas are
Take the difference between each team's feature
Concatenate the featers IE for one training example it would be [A_1,B_1,A_2,B_2,...,A_n,B_n] where n is the feature number
Use one feature vector for each team?(Don't know if that works)
Could anyone give somee suggestions
While I agree with Scott Hunter to try everything, here are a few thoughts:
Taking the difference - this depends very much on what the team's features represent. If each feature is like a statistic of the team (such as win rate, etc), then taking the difference might work. If it is something more abstract, it may not be a good idea. But you can try.
Concatenate the features - I think this would be a good choice, at least when starting out. It definitely seems the most obvious, and I think it would work to a good extent.
Another approach: You could build an encoder, which takes the team's feature vector and outputs a condensed representation. Then, you can do something with this condensed representation (feed it to a simpler model, or another MLP as well).
I'm looking for a way to differentiate between 3 classes(classification problem) for each OBJECT to classify.
I have a large dataset(millions of lines). There are 2 features, each have 100 values(scaled to 0-1).
Each line refers to one sample of a specific Object(Object_id, 100 columns of my first feature, 100 of my second feature).
Each object(that has to be classified to either 3 classes) have at least 100 samples(1 sample is 1 line)
Unfortunately Classe 3 counts only 1/10 compared to 1 and 2(each object of classe 3 have around 500 samples, however classe 1 and 2 objects have around 2000 and more).
In order to do the classification, I need to take a bach of samples for each object(for exmaple 20, 50, or 100).
I dont know what algo suites better for my case, I'm new to deep learning so bear with me please
Let's break this down to two main questions: how to handle unbalanced datasets and which model to use.
Unbalanced datasets
Most machine learning algorithms are sensitive to some degree on unbalanced datasets. This is a huge challenge for Machine Learning in fields like medical diagnostics or seismology, where you have 98% "normal" readings and 2% "event" readings. There is no silver bullet to this problem. Some algorithms are more resilient to an unbalanced dataset, and some that deliberately unbalance their datasets to encourage a strong model (see bagging), and there are options to augment your data by introducing cloned data with statistical noise. However, your easiest and most effective approach is to decimate your dataset to make it balanced.
You have a class split of 2000|2000|500 datapoints. Randomly sample 500 datapoints from each of the first two classes so you have a balanced 500|500|500 dataset. It is important to randomly sample, instead of simply taking the first 500 as you want a representative sample of the class population. see the numpy.random module for how to select your datapoints.
Model selection
Although Deep Learning is portrayed as the be-all and end-all for machine learning, it represents a significant amount of time and cost to prepare, train and monitor. A typical approach to any new problem is to try some "baseline" shallow learning models. Often you'll see the following scenarios:
Your baseline models fail to train.
Your baseline model trains and fits moderately
Your baseline model trains and fits closely
In the first scenario, your deep learning model is unlikely to train either. In the third scenario there is no need to build a deep learning model when a simpler algorithm can solve it. Scenario 2 is your candidate fro deep learning.
So what models could you use?
Well, we know that it's a supervised problem, that we have a good number of samples, and that we are looking to classify. Your best bet for this kind of question is a Random Forests model. There is a good simple implementation in scikit-learn and hundreds of tutorials.
Alternatively, if you're looking at class fit through clustering, K-means ++ models (and co), or even Gaussian Mixture Models are a good place to start (again, see scikit learn's sklearn.clustering and sklearn.mixture)
If it fits well, then your work is done. If it fits moderately, think about deep learning. If it fails to fit, get add more features (and more diverse features) to your dataset.
I want to do sentiment analysis using machine learning (text classification) approach. For example nltk Naive Bayes Classifier.
But the issue is that a small amount of my data is labeled. (For example, 100 articles are labeled positive or negative) and 500 articles are not labeled.
I was thinking that I train the classifier with labeled data and then try to predict sentiments of unlabeled data.
Is it possible?
I am a beginner in machine learning and don't know much about it.
I am using python 3.7.
Thank you in advance.
Is it possible to train the sentiment classification model with the labeled data and then use it to predict sentiment on data that is not labeled?
Yes. This is basically the definition of what supervised learning is.
I.e. you train on data that has labels, so that you can then put it into production on categorizing your data that does not have labels.
(Any book on supervised learning will have code examples.)
I wonder if your question might really be: can I use supervised learning to make a model, assign labels to another 500 articles, then do further machine learning on all 600 articles? Well the answer is still yes, but the quality will fall somewhere between these two extremes:
Assign random labels to the 500. Bad results.
Get a domain expert assign correct labels to those 500. Good results.
Your model could fall anywhere between those two extremes. It is useful to know where it is, so know if it is worth using the data. You can get an estimate of that by taking a sample, say 25 records, and have them also assigned by a domain expert. If all 25 match, there is a reasonable chance your other 475 records also have been given good labels. If e.g. only 10 of the 25 match, the model is much closer to the random end of the spectrum, and using the other 475 records is probably a bad idea.
("10", "25", etc. are arbitrary examples; choose based on the number of different labels, and your desired confidence in the results.)
I am trying to build an RL agent to price paid for airline seats (not the ticket). The general set up is:
After choosing their flights (for n people on a booking), a customer will view a web page with the available seat types and their prices visible.
They select between zero and n seats from a seat map with a variety of different prices for different seats, to be added to their booking.
After perhaps some other steps, they pay for the booking and the agent is rewarded with the seat revenue.
I have not decided on a general architecture yet. I want to take various booking and flight information into account, so I know I will be using function approximation (most likely a neural net) to generalise over the state space.
However, I am less clear on how to set up my action space. I imagine an action would amount to a vector with a price for each different seat type. If I have, for example, 8 different seat types, and 10 different price points for each, this gives me a total of 10^8 different actions, many of which will be very similar. Additionally, each sub-action (pricing one seat type) is somewhat dependent on the others, in the sense that the price of one seat type will likely affect the demand (and hence reward contribution) for another. Hence, I doubt the problem can be decomposed into a set of sub-problems.
I'm interested if there has been any research into dealing with a problem like this. Clearly any agent I build needs some way to generalise across actions to some degree, since collecting real data on millions of actions is not possible, even just for one state.
As I see it, this comes down to two questions:
Is it possible to get an agent to understand actions in relative terms? Say for example, one set of potential prices is [10, 12, 20]. Can I get my agent to realise that there is a natural ordering there, and that the first two pricing actions are more similar to each other than to the third possible action?
Further to this, is it possible to generalise from this set of actions - could an agent be set up to understand that the set of prices [10, 13, 20] is very similar to the first set?
I haven't been able to find any literature on this, especially relating to the second question - any help would be much appreciated!
Correct me if I'm wrong, but I am going to assume this is what you are asking and will answer accordingly.
I am building an RL and it needs to be smart enough to understand that if I were to buy one airplane ticket, it will subsequently affect the price of other airplane tickets because there is now less supply.
Also, the RL agent must realize that actions very close to each other are relatively similar actions, such as [10, 12, 20] ≈ [10, 13, 20]
1) In order to provide memory to your RL agent, you can do this in two ways. The easy way is to feed the states as a vector of past purchased tickets, as well as the current ticket.
Example: Let's say we build the RL to remember at least the past 3 transactions. At the very beginning, our state vector will be [0, 0, 3], meaning that there was no purchases of tickets previously (the zeros), and currently, we are purchasing ticket #3. Then, the next time step's state vector can be [0, 3, 6], telling the RL agent that previously, ticket #3 has been picked, and now we're buying ticket #6. The neural network will learn that the state vector [0, 0, 6] should map to a different outcome compared to [0, 3, 6], because in the first case, ticket #6 was the first ticket purchased and there was lots of supply. But in the 2nd case, ticket 3 was already sold, so now all the remaining tickets went up in price.
The proper and more complex way would be to use a recurrent neural network as your function approximator for your RL agent. The recurrent neural network architecture allows for certain "important" states to be remembered by the neural network. In your case, the amount of tickets purchased previously is important, so the neural network will remember the previously purchased tickets, and calculate the output accordingly.
2) Any function approximation reinforcement learning algorithm will automatically generalize sets of actions close to each other. The only RL architectures that would not do this would be tabular based approaches.
The reason is the following:
We can think of these function approximators simply as a line. Neural networks simply build a highly nonlinear continuous line (neural networks are trained using backpropagation and gradient descent, so they must be continuous), and a set of states will map to a unique set of outputs. Because it is a line, sets of states that are very similar SHOULD map to outputs that are also very close. In the most basic case, imagine y = 2x. If our input x = 1, our y = 2. And if our input x is 1.1, which is very close to 1, our output y = 2.2, which is very close to 2 because they are on a continuous line.
For tabular approach, there is simply a matrix. On the y axis, you have the states, and on the x axis, you have the actions. In this approach, the states and actions are discrete. Depending on the discretization, the difference can be massive, and if the system is poorly discretized, the actions very close to each other MAY not be generalized.
I hope this helps, please let me know if anything is unclear.
I would like to use one of Caffe's reference model i.e. bvlc_reference_caffenet. I found that my target class i.e. person is one of the classes included in the ILSVRC dataset that has been trained for the model. As my goal is to classify whether a test image contains a person or not, I may achieve this by the following:
Use inference directly with 1000 number of output. This doesn't
require training/learning.
Change the network topology a little bit with the final FC layer's number of output (num_output) is set to 2 (instead of 1000). Retrain it as a binary classification problem.
My concern is about computational effort at deployment/prediction phase (testing). The latter looks more expensive computationally than the former. This is because during prediction phase it needs to compute those 1000 output possibilities to find the one with the highest score. What I'm not sure is that, it could be the case that there's a heuristic (which I'm not aware of) that simplifies the computation.
Can somebody please help cross check my understanding on this.