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A model underfits when it is too simple with regards to the data it is trying to model. One way to detect such situation is to use the bias–variance approach, which can represented like this: Your model is underfitted when you have a high bias. To know whether you have a too high bias or a too high variance, you view the phenomenon in terms of training ...


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Hyperparameters and parameters are often used interchangeably but there is a difference between them. You call something a 'hyperparameter' if it cannot be learned within the estimator directly. However, 'parameters' is more general term. When you say 'passing the parameters to the model', it generally means a combination of hyperparameters along with some ...


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In addition to the answer above. Model parameters are the properties of the training data that are learnt during training by the classifier or other ml model. For example in case of some NLP task: word frequency, sentence length, noun or verb distribution per sentence, the number of specific character n-grams per word, lexical diversity, etc. Model ...


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The Dirichlet distribution is a multivariate distribution. We can denote the parameters of the Dirichlet as a vector of size K of the form ~$\frac{1}{B(a)} \cdot \prod\limits_{i} x_i^{a_{i-1}}$, where $a$ is the vector of size $K$ of the parameters, and $\sum x_i = 1$. Now the LDA uses some constructs like: a document can have multiple topics (because of ...


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Assuming symmetric Dirichlet distributions (for simplicity), a low alpha value places more weight on having each document composed of only a few dominant topics (whereas a high value will return many more relatively dominant topics). Similarly, a low beta value places more weight on having each topic composed of only a few dominant words.


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Hyper-parameters are those which we supply to the model, for example: number of hidden Nodes and Layers,input features, Learning Rate, Activation Function etc in Neural Network, while Parameters are those which would be learned by the machine like Weights and Biases.


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To answer your question it is important to understand the frame of reference you are looking for, if you are looking for what philosophically you are trying to achieve in model fitting, check out Rubens answer he does a good job of explaining that context. However, in practice your question is almost entirely defined by business objectives. To give a ...


8

A google search for r-squared adjusted yielded several easy to follow explanations. I am going to paste a few directly from such results. Meaning of Adjusted R2 Both R2 and the adjusted R2 give you an idea of how many data points fall within the line of the regression equation. However, there is one main difference between R2 and the adjusted R2: R2 ...


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Is there any model in machine learning that does not have parameters? Yes. k-nearest neighbors is parameterless (there is only a single hyper-parameter $k$). If such parameterless models exist, what are their purpose then? Isn't it the whole point of training to tune a model's parameters? Exactly: such models require no training at all. k-NN in ...


7

This paper Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement by Forman and Scholz discuss the different methods for computing the average F-score in cross validation. The paper shows that under very high class imbalance some of the computation methods (average of individual folds F-scores or F-score based on average ...


7

In machine learning, a model $M$ with parameters and hyper-parameters looks like, $Y \approx M_{\mathcal{H}}(\Phi | D)$ where $\Phi$ are parameters and $\mathcal{H}$ are hyper-parameters. $D$ is training data and $Y$ is output data (class labels in case of classification task). The objective during training is to find estimate of parameters $\hat{\Phi}$ ...


7

Models are but abstractions of what is seen in real life. They are designed in order to abstract-away nitty-gritties of the real system in observation, while keeping sufficient information to support desired analysis. If a model is overfit, it takes into account too many details about what is being observed, and small changes on such object may cause the ...


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Generally people perform a grid search, which in its simplest "exhaustive" form is similar to Method 1. However there are also more 'intelligent' ways to choose what to explore, which optimize in parameter space in a fashion similar to how each individual model is optimized. It can be tricky to do greedy optimization in this space, as it is often strongly ...


5

Simply, one common approach is to increase the complexity of the model, making it simple, and most probably underfitting at first, and increasing the complexity of the model until early signs of overfitting are witnessed using a resampling technique such as cross validation, bootstrap, etc. You increase the complexity either by adding parameters (number of ...


4

In simplified words, Model Parameters are something that a model learns on its own. For example, 1) Weights or Coefficients of independent variables in Linear regression model. 2) Weights or Coefficients of independent variables SVM. 3) Split points in Decision Tree. Model hyper-parameters are used to optimize the model performance. For example, 1)...


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The C being a regularized parameter, controls how much you want to punish your model for each misclassified point for a given curve. If you put large value to C it will try to reduce errors but at the same time it may happen that it would not perform better on test dataset hence cause overfitting. To get to know more about effect of C in svm. Refer this.


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I just want to add more information about these more "intelligent" ways to pick hyperparameters. One in particular that's becoming more and more popular. BAYESIAN OPTIMIZATION OF MACHINE LEARNING ALGORITHMS by Jasper Snoek, Hugo Larochelle and Ryan P. Adam. Which has proven effective in algorithms including latent Dirichlet allocation, structured SVMs ...


3

CAPM (Capital Asset Pricing Model) in Finance is a classic example of an underfit model. It was built on the beautiful theory that "Investors only pay for risk they can't diversify away" so expected excess returns are equal to correlation to market returns. As a formula [0] Ra = Rf + B (Rm - Rf) where Ra is the expected return of the asset, Rf is the risk ...


2

In general, there is no guarantee that ANNs such as a multi-layer Perceptron network will converge to the global minimum squared error (MSE) solution. The final state of the network can be heavily dependent on how the network weights are initialized. Since most initialization schemes (including Nguyen-Widrow) use random numbers to generate the initial ...


2

If an exhaustive nonlinear scan is too expensive and a linear scan doesn't yield the best results then I suggest you try a stochastic nonlinear search i.e. a random search for hyperparameter optimization. Scikit learn has a user friendly description in its user guide. Here is a paper on random search for hyperparameter optimization.


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One simple idea, no imputation needed: build a model using the parameters have always existed, then each time a new set of parameters gets added, use them to model the residual of the previous model. Then you can sum the contributions of all the models that apply to the data you happen to have. (If effects tend to multiply rather than add, you could do this ...


2

As mentioned by other users, the solution is not very clear. The general approach is to follow what is mentioned here. Also, as suggested by one of the senior R&D employee and my mentor, the method in practice is to calculate average f1-score as the HM of average precision and average recall. This surely depends on your usecase as well as how you are ...


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Here's one (admittedly hard) way. If you really want to understand the low-level details, you can always work through the source code. For example, we can see that the LinearSVC fit method calls _fit_liblinear. That calls train_wrap in liblinear, which gets everything ready to call into the C++ function train. So train in linear.cpp is where the heavy ...


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Common use cases include: Fraud detection Transactions volume prediction Next transaction date Fraud detection This is usually tackled with anomaly detection. It requires information on the two transaction parties and using machine learning to figure out when a transaction is out of the norm and flagging as a potential case of fraud. Transactions volume ...


1

Consider the case of the majority rule. In the majority rule you go over the training set, check which concept value is the majority and returns it for every sample. There are no parameters, there is a training process and the classifier is has some value (mostly as a benchmark or in extreme cases). Note the the word "parameter" has different meanings and ...


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My understanding is that $\eta$ is set before the training starts to a large value but then, as the training progresses and the function gets closer and closer to a local minimum, the learning rate is decreased. In that case, doesn't the learning parameter satisfy both the definitions of a parameter and of a hyper-parameter? No it does not, because you are ...


1

y = -(coef_0 / coef_1) x - intercept/coef_1 is coef_0 x + coef_1 y + intercept = 0, which is the border line separating the blob from the rest. (coef_0, coef_1) is the normal vector and the direction indicates where the blob is.


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If the old variables and the new variables are highly correlated then you could do a more advanced form of imputation and make a model for each new input that predicts the new input given the old inputs. This model would probably be pretty effective good at predicting the new inputs because, as you said, there is a strong correlation among the inputs. Then ...


1

A good answer to this question has to rely on the specific dataset / domain. The questions I would ask myself are (in this order): Can I solve my classification task without those features? → Just remove those features Do I know of a relationship between features I know and the (partially) missing feature? → Find hard rules to fill those missing ones ...


1

In the vein of bayesian optimization, I prefer Hyperopt, available on github at https://github.com/hyperopt/hyperopt or through pip, homepage of author at https://github.com/hyperopt/hyperopt. The tree-parzen-estimator algorithm behind it is described in the paper at http://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf . You can ...


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