I am training my dataset using a multivariable (say, 10) linear regression model. I have to choose the parameters but it results in overfitting. I have read that using the genetic algorithm for tuning my parameters we may achieve the minimum possible cost function error.
I am a complete beginner in this area and am unable to understand how the genetic algorithm can help in choosing the parameters using the parent's MSE. Any help is appreciated.
Related
while selecting features in machine learning, one can use Lasso regression to figure out the least required feature by selecting the least coefficient but we can do the same using Linear Regression
linear regression
Y=x0+x1b1+x2b2.......xnbn
here x1,x2,x3...xn are coefficient, using gradient descent we get the best coefficient, we can remove the features who has the least coefficient. now when it is possible using Linear Regression then why should one use Lasso Regression?
am i missing something, please help
Lasso is a regularization technique which is for avoiding overfitting when you train your model. When you do not use any regularization technique, your loss function just tries to minimize the difference between the predicted value and real value min |y_pred - y|.
To minimize this loss function, gradient descent changes the coefficient of your model. This step may cause the overfitting of your model because your optimization function want only to minimize the difference between prediction and real value. To solve this issue, regularization techniques add another penalty term to the loss functions: value of coefficients. In this way, when your model tries to minimize the difference between predicted and real value, it also tries to do not increase the coefficients too much.
As you mentioned, you can select features in both ways, however, Lasso technique also takes care of the overfitting problem.
I am trying to solve a regression problem by predicting a continuous value using machine learning. I have a dataset which composed of 6 float columns.
The data come from low price sensors, this explain that very likely we will have values that can be considered out of the ordinary. To fix the problem, and before predicting my continuous target, I will predict data anomalies, and use him as a data filter, but the data that I have is not labeled, that's mean I have unsupervised anomaly detection problem.
The algorithms used for this task are Local Outlier Factor, One Class SVM, Isolation Forest, Elliptic Envelope and DBSCAN.
After fitting those algorithms, it is necessary to evaluate them to choose the best one.
Can anyone have an idea how to evaluate an unsupervised algorithm for anomaly detection ?
The only way is to generate synthetic anomalies which mean to introduce outliers by yourself with the knowledge of how a typical outlier will look like.
can we apply only genetic algorithm model on a dataset for linear regression?
for example:
assume we have a dataset with features such as toffle score, cgpa, gre score ,etc and output values of chance of admission. In this we have to predict the chances of admission based on the features.Link to the dataset
Lot of things are possible by using genetic algorithms. You just have to be sore that you are using correct dataset, you have to know what you want to get from it and last but not least, you have to know what exactly are you doing, which means you need to have correct fitness function :)
According to my understanding, RF selects features randomly and hence is hard to overfit. But, in sklearn Gradient boosting also offers the option of max_features which can help to prevent overfitting. So, why would anyone use Random forest?
Can anyone explain when to use Gradient boosting vs Random forest based on the given data?
Any help is highly appreciated.
According to my personal experience, Random Forest could be a better choice when..
You train a model on small data set.
Your data set has few features to learn.
Your data set has low Y flag count or you try to predict a situation that has low chance to occur or rarely occurs.
In these situations, Gradient Boosting algorithms like XGBoost and Light GBM can overfit (though their parameters are tuned) while simple algorithms like Random Forest or even Logistic Regression may perform better. To illustrate, for XGboost and Ligh GBM, ROC AUC from test set may be higher in comparison with Random Forest but shows too high difference with ROC AUC from train set.
Despite the sharp prediction form Gradient Boosting algorithms, in some cases, Random Forest take advantage of model stability from begging methodology (selecting randomly) and outperform XGBoost and Light GBM. However, Gradient Boosting algorithms perform better in general situations.
Similar question asked on Quora:
https://www.quora.com/How-do-random-forests-and-boosted-decision-trees-compare
I agree with the author at the link that random forests are more robust -- they don't require much problem-specific tuning to get good results. Besides that, a couple other items based on my own experience:
Random forests can perform better on small data sets; gradient boosted trees are data hungry
Random forests are easier to explain and understand. This perhaps seems silly but can lead to better adoption of a model if needed to be used by less technical people
I think that's also true. I have also read on this page How Random Forest Works
There explains the advantages of random forest. like this :
For applications in classification problems, Random Forest algorithm
will avoid the overfitting problem
For both classification and
regression task, the same random forest algorithm can be used
The Random Forest algorithm can be used for identifying the most
important features from the training dataset, in other words,
feature engineering.
I'm working on binary classification problem using Apache Mahout. The algorithm I use is OnlineLogisticRegression and the model which I currently have strongly tends to produce predictions which are either 1 or 0 without any middle values.
Please suggest a way to tune or tweak the algorithm to make it produce more intermediate values in predictions.
Thanks in advance!
What is the test error rate of the classifier? If it's near zero then being confident is a feature, not a bug.
If the test error rate is high (or at least not low), then the classifier might be overfitting the training set: measure the difference between of the training error and the test error. In that case, increasing regularization as rrenaud suggested might help.
If your classifier is not overfitting, then there might be an issue with the probability calibration. Logistic Regression models (e.g. using the logit link function) should yield good enough probability calibrations (if the problem is approximately linearly separable and the label not too noisy). You can check the calibration of the probabilities with a plot as explained in this paper. If this is really a calibration issue, then implementing a custom calibration based on Platt scaling or isotonic regression might help fix the issue.
From reading the Mahout AbstractOnlineLogisticRegression docs, it looks like you can control the regularization parameter lambda. Increasing lambda should mean your weights are closer to 0, and hence your predictions are more hedged.