We changed our privacy policy. Read more.
30

The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing. Dealing with such a Model: Data Preprocessing: Standardizing and Normalizing the data. Model compelxity: Check if the model is too complex. Add dropout, reduce number of layers or number of neurons in each layer. Learning Rate and Decay ...


24

A baseline is the result of a very basic model/solution. You generally create a baseline and then try to make more complex solutions in order to get a better result. If you achieve a better score than the baseline, it is good.


20

You might want to interpret your coefficients. That is, to be able to say things like "if I increase my variable $X_1$ by 1, then, on average and all else being equal, $Y$ should increase by $\beta_1$". For your coefficients to be interpretable, linear regression assumes a bunch of things. One of these things is no multicollinearity. That is, your $X$ ...


18

That paper gives a nice answer, where i quoted from. Search for Should I standardize the target variables (column vectors)? in that page. Standardizing target variables is typically more a convenience for getting good initial weights than a necessity. However, if you have two or more target variables and your error function is scale-sensitive like the ...


17

Consider an equation of the form $y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \epsilon$ where $x$'s are the variables and $\beta$'s are the parameters. Here, y is a linear function of $\beta$'s (linear in parameters) and also a linear function of $x$'s (linear in variables). If you change the equation to $y = \beta_0 + \beta_1x_1 + \beta_2x_1^2 + \epsilon$ ...


17

What you are describing is a normal multidimensional linear regression. This type of problem is normally addressed with a feed-forward network, either MLP or any other architecture that suits the nature of the problem. Any neural network framework is able to do something like that. The key to do that is to remember that the last layer should have linear ...


17

A baseline is a method that uses heuristics, simple summary statistics, randomness, or machine learning to create predictions for a dataset. You can use these predictions to measure the baseline's performance (e.g., accuracy)-- this metric will then become what you compare any other machine learning algorithm against. In more detail: A machine learning ...


17

Your data can be put into a pandas DataFrame using import pandas as pd data = {'Loan ID': ['LP001002', 'LP001003', 'LP001005', 'LP001006', 'LP001008'], 'Married': ['No', 'Yes', 'Yes', 'Yes', 'No'], 'Dependents': [0, 1, 0, 0, 0], 'Education': ['Graduate', 'Graduate', 'Graduate', 'Not Graduate', 'Graduate'], 'Self_Employed': ['...


15

Scikit-learn indeed does not support stepwise regression. That's because what is commonly known as 'stepwise regression' is an algorithm based on p-values of coefficients of linear regression, and scikit-learn deliberately avoids inferential approach to model learning (significance testing etc). Moreover, pure OLS is only one of numerous regression ...


15

Simply put because one level of your categorical feature (here location) become the reference group during dummy encoding for regression and is redundant. I am quoting form here "A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. This amounts to a linear hypothesis on the level means." This ...


14

You can still use sklearn.linear_model.LinearRegression. Simply make the output y a matrix with as many columns as you have dependent variables. If you want something non-linear, you can try different basis functions, use polynomial features, or use a different method for regression (like a NN).


11

If I understand you correctly, this is the case of multiple linear regression with sparse data (sparse regression). Assuming that, I hope you will find the following resources useful. 1) NCSU lecture slides on sparse regression with overview of algorithms, notes, formulas, graphics and references to literature: http://www.stat.ncsu.edu/people/zhou/courses/...


11

There is no technique that will eliminate the risk of overfitting entirely. The methods you've listed are all just different ways of fitting a linear model. A linear model will have a global minimum, and that minimum shouldn't change regardless of the flavor of gradient descent that you're using (unless you're using regularization), so all of the methods you'...


11

Yes this is possible by treating the audio as a sequence into a Recurrent Neural Network (RNN). You can train a RNN against a target that is correct at the end of a sequence, or even to predict another sequence offset from the input. Do note however that there is a bit to learn about options that go into the construction and training of a RNN, that you will ...


11

from xgboost import XGBClassifier model = XGBClassifier.fit(X,y) # importance_type = ['weight', 'gain', 'cover', 'total_gain', 'total_cover'] model.get_booster().get_score(importance_type='weight') However, the method below also returns feature importance's and that have different values to any of the "importance_type" options in the method above. This was ...


10

First of all, word "sample" is normally used to describe subset of population, so I will refer to the same thing as "example". Your SGD implementation is slow because of this line: for each training example i: Here you explicitly use exactly one example for each update of model parameters. By definition, vectorization is a technique for converting ...


10

If you look at the Keras documentation, you will observe that for Sequential model's first layers takes the required input. So for example, your first layer is Dense layer with input dimension as 400. Hence each input should be a numpy array of size 400. You can pass a 2D numpy array with size (x,400). (I assume that x is the number of input examples). Your ...


9

Word of warning from a former airline Revenue Management analyst: you might be barking up the wrong tree with this approach. Apologies for the wall of text that follows, but this data is a lot more complex and noisy than might appear at first glance, so wanted to provide a short description of how it's generated; forewarned is forearmed. Airline fares have ...


9

How can I interpret RMSE? RMSE is exactly what's defined. $24.5 is the square root of the average of squared differences between your prediction and your actual observation. Taking squared differences is more common than absolute difference in statistics, as you might have learnt from the classical linear regression. It confuses me a little. And I could ...


9

Method in Python One way to check the correlation of every feature against the target variable is to run the code: # Your data should be a pandas dataframe for this example import pandas yourdata = ... corr_matrix = yourdata.corr() print(corr_matrix["your_target_variable"].sort_values(ascending=False)) The following correlation output should list all the ...


8

In this context probably the plain English way to put it is that 'returns increase with additional exposure to the ad, but there is a tapering effect at the upper end of exposures. Looking at this picture of a parabola here (i.e a graph of y= -ax^2+bx+c): Most likely your returns data are between 0 and the peak so you don't actually see a decline, just a ...


8

Your network design/logic is basically correct, but you are seeing some very common problems with neural network numerical stability. This results in your weights diverging and not training accurately. Here are the fixes, any one of them might help a little, but the first two should be used for nearly all neural network projects. 1. Inputs need to be ...


8

This will compute the sigmoid of a scalar, vector or matrix. function g = sigmoid(z) % SIGMOID Compute sigmoid function % g = SIGMOID(z) computes the sigmoid of z. % Compute the sigmoid of each value of z (z can be a matrix, % vector or scalar). SIGMOID = @(z) 1./(1 + exp(-z)); g = SIGMOID(z); end


8

No matter the model, you can always use the non-parametric bootstrap to construct a confidence interval for any parameter, including predictions (which are actually random variables themselves but are reported as expectations). Here's the general procedure: Let $N$ denote the number of observations in your training data $X$, and $x_j$ denote the specific ...


8

regplot() performs a simple linear regression model fit and plot. lmplot() combines regplot() and FacetGrid. The FacetGrid class helps in visualizing the distribution of one variable as well as the relationship between multiple variables separately within subsets of your dataset using multiple panels. lmplot() is more computationally intensive and is ...


8

Yes, gradient boosted trees can make predictions outside the training labels' range. Here's a quick example: from sklearn.datasets import make_classification from sklearn.ensemble import GradientBoostingRegressor X, y = make_classification(random_state=42) gbm = GradientBoostingRegressor(max_depth=1, n_estimators=10, ...


8

The direct way to check your model for overfitting is to compare its performance on a training set with its performance on a testing set; overfitting is when your train score is significantly above your cv score. According to your comments, your r2 score is 0.97 on the training set, and 0.86 on your testing set (or similarly, 0.88 cv score, mean across 10 ...


7

Aleksandr's answer is completely correct. However, the way the question is posed implies that this is a straightforward ordinary least squares regression question: minimizing the sum of squared residuals between a dependent variable and a linear combination of predictors. Now, while there may be many zeros in your design matrix, your system as such is not ...


7

The problem is your model choice, as you seem to recognize. In the case of linear regression, there is no restriction on your outputs. Often this is fine when predictions need to be non-negative so long as they are far enough away from zero. However, since many of your training examples are zero-valued, this isn't the case. If your data is non-negative ...


7

No, currently there isn't a package in Python that does segmented linear regression as thoroughly as those in R (e.g. R packages listed in this blog post). Alternatively, you can use a Bayesian Markov Chain Monte Carlo algorithm in Python to create your segmented model. Segmented linear regression, as implemented by all the R packages in the above link, ...


Only top voted, non community-wiki answers of a minimum length are eligible