Questions tagged [supervised-learning]

Supervised learning is a type of machine learning algorithm that learns a mapping function y = f(x) between input variables (x) and output variables (y). The two most common supervised learning tasks are classification and regression.

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).

372 questions
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What would I prefer - an over-fitted model or a less accurate model?

Let's say we have two models trained. And let's say we are looking for good accuracy. The first has an accuracy of 100% on training set and 84% on test set. Clearly over-fitted. The second has an accuracy of 83% on training set and 83% on test set.…
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What kinds of learning problems are suitable for Support Vector Machines?

What are the hallmarks or properties that indicate that a certain learning problem can be tackled using support vector machines? In other words, what is it that, when you see a learning problem, makes you go "oh I should definitely use SVMs for…
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Merging sparse and dense data in machine learning to improve the performance

I have sparse features which are predictive, also I have some dense features which are also predictive. I need to combine these features together to improve the overall performance of the classifier. Now, the thing is when I try to combine these…
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Supervised learning vs reinforcement learning for a simple self driving rc car

I'm building a remote-controlled self driving car for fun. I'm using a Raspberry Pi as the onboard computer; and I'm using various plug-ins, such as a Raspberry Pi camera and distance sensors, for feedback on the car's surroundings. I'm using OpenCV…
Ryan Zotti
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Which supervised learning algorithms are available for matching?

I'm working on a non-profit where we try to help potential university applicants by matching them with alumni that want to share their experience/wisdom and, at the moment, it is happening manually. So I'll have two tables, one with students and one…
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Is max_depth in scikit the equivalent of pruning in decision trees?

I was analyzing the classifier created using a decision tree. There is a tuning parameter called max_depth in scikit's decision tree. Is this equivalent of pruning a decision tree? If not, how could I prune a decision tree using scikit? dt_ap =…
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Why neural networks do not perform well on structured data?

I was recently working on some classification problem where decision trees performed better than neural networks. I had tried various combinations with neural networks altering the number of neurons / hidden layers with an objective to beat the…
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Neural network with flexible number of inputs?

Is it possible to create a neural network which provides a consistent output given that the input can be in different length vectors? I am currently in a situation where I have sampled a lot of audio files, which are of different length, and have to…
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Ideas for prospect scoring model

I have to think about a model to identify prospects (companies) that have a high chance of being converted into clients, and I'm looking for advice on what kind of model could be of use. The databases I will have are, as far as I know (I don't have…
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When is the sum of models the model of the sum?

The response variable in a regression problem, $Y$, is modeled using a data matrix $X$. In notation, this means: $Y$ ~ $X$ However, $Y$ can be separated out into different components that can be modeled independently. $$Y = Y_1 + Y_2 + Y_3$$ Under…
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Why will the accuracy of a highly unbalanced dataset reduce after oversampling?

I have created a synthetic dataset, with 20 samples in one class and 100 in the other, thus creating an imbalanced dataset. Now the accuracy of classification of the data before balancing is 80% while after balancing (i.e., 100 samples in both the…
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Is there any difference between a weak learner and a weak classifier?

While reading about decision tree ensembles Gradient Boosting, AdaBoost etc. I have found the following two concepts weak learner and weak classifier. Are they the same? If there is any difference what is it?
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Why does feature scaling improve the convergence speed for gradient descent?

From this article, it says: We can speed up gradient descent by scaling. This is because θ will descend quickly on small ranges and slowly on large ranges, and so will oscillate inefficiently down to the optimum when the variables are very…
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2 answers

Is NN with no hidden layer is behave like a regression?

Is a NN with no hidden layer is behave like a regression? What we could say that NN without hidden layer can say us? ​ If we have for instance 20 input and 4 output and I have no true label, is it similar to regression? If it is a regression then it…
user10296606
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Identifying sequences of actions required to complete tasks, based on data of completed tasks

I have a list of about 20 tasks that a machine need to be performed by a machine. Each tasks consists of a sequence of 3 to 5 actions that must be executed sequentially to complete a task. There are a total of 50 different actions. I want an…
SnShines
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