I am working on KNN algorithm, I have some questions please I need answers:
I tried different values of K such as 3, 5, 7, and sqrt(n)=73. I get different accuracies according to these different values of K. What K should I use in my model and why ??
What is the best percentage that I should use to split the dataset into train and test sets ??
why the accuracy of the train set is always greater than the accuracy of test set ??
Which accuracy (train accuracy or test accuracy) is used to describe the overall model accuracy ??
Choosing the value of K is not a exact science. In this post, user20160 explained a procedure to choose a good K using k-fold cross validation.
Usually, 80/20 and 70/30 ratios are used but once again, it is not a absolute truth. If your train set ratio is too large, your model could overfit which means your model learns specially for the train set and will not be performant with real cases. On another hand, if your train set is too small, your model could underfit.
The accuracy of the train set is often greater than the test one because your model is only trained with the train set and test dataset are cases your model has never seen before. It is like learning to ride a bike with only one bike and being evaluated with another bike.
The train accuracy is not realistic to evaluate the performance of your model since train dataset is well known by your model. Test accuracy is more relevant because test dataset is new and has never been seen by your model.
To better understand everything about model evaluation I strongly recommend you to take look at the cross validation link above.
Related
I am using multi linear regression to do sales quantity forecasting in retail. Due to practical issues, I cannot use use ARIMA or Neural Networks.
I split the historical data into train and validation sets. Using a walk forward validation method would be computationally quite expensive at this point. I have to take x number of weeks preceding current date as my validation set. The time series prior to x is my training set. The problem I am noting with this method is that accuracy is far higher during the validation period as compared to the future prediction. That is, the further we move from the end of the training period, the less accurate the prediction / forecast. How best can I control this problem?
Perhaps a smaller validation period, will allow the training period to come closer to the current date and hence provide a more accurate forecast; but this hurts the value of validation.
Another thought is to cheat and give both the training and validation historical data during training. As I am not using neural nets, the selected algo should not be over-fitted. Please correct me if this assumption is not right.
Any other thoughts or solution would be most welcome.
Thanks
Regards,
Adeel
If you're not using ARIMA or DNN, how about using rolling windows of regressions to train and test the historical data?
I was reading about cross validation and about how it it is used to select the best model and estimate parameters , I did not really understand the meaning of it.
Suppose I build a Linear regression model and go for a 10 fold cross validation, I think each of the 10 will have different coefficiant values , now from 10 different which should I pick as my final model or estimate parameters.
Or do we use Cross Validation only for the purpose of finding an average error(average of 10 models in our case) and comparing against another model ?
If your build a Linear regression model and go for a 10 fold cross validation, indeed each of the 10 will have different coefficient values. The reason why you use cross validation is that you get a robust idea of the error of your linear model - rather than just evaluating it on one train/test split only, which could be unfortunate or too lucky. CV is more robust as no ten splits can be all ten lucky or all ten unfortunate.
Your final model is then trained on the whole training set - this is where your final coefficients come from.
Cross-validation is used to see how good your models prediction is. It's pretty smart making multiple tests on the same data by splitting it as you probably know (i.e. if you don't have enough training data this is good to use).
As an example it might be used to make sure you aren't overfitting the function. So basically you try your function when you've finished it with Cross-validation and if you see that the error grows a lot somewhere you go back to tweaking the parameters.
Edit:
Read the wikipedia for deeper understanding of how it works: https://en.wikipedia.org/wiki/Cross-validation_%28statistics%29
You are basically confusing Grid-search with cross-validation. The idea behind cross-validation is basically to check how well a model will perform in say a real world application. So we basically try randomly splitting the data in different proportions and validate it's performance. It should be noted that the parameters of the model remain the same throughout the cross-validation process.
In Grid-search we try to find the best possible parameters that would give the best results over a specific split of data (say 70% train and 30% test). So in this case, for different combinations of the same model, the dataset remains constant.
Read more about cross-validation here.
Cross Validation is mainly used for the comparison of different models.
For each model, you may get the average generalization error on the k validation sets. Then you will be able to choose the model with the lowest average generation error as your optimal model.
Cross-Validation or CV allows us to compare different machine learning methods and get a sense of how well they will work in practice.
Scenario-1 (Directly related to the question)
Yes, CV can be used to know which method (SVM, Random Forest, etc) will perform best and we can pick that method to work further.
(From these methods different models will be generated and evaluated for each method and an average metric is calculated for each method and the best average metric will help in selecting the method)
After getting the information about the best method/ or best parameters we can train/retrain our model on the training dataset.
For parameters or coefficients, these can be determined by grid search techniques. See grid search
Scenario-2:
Suppose you have a small amount of data and you want to perform training, validation and testing on data. Then dividing such a small amount of data into three sets reduce the training samples drastically and the result will depend on the choice of pairs of training and validation sets.
CV will come to the rescue here. In this case, we don't need the validation set but we still need to hold the test data.
A model will be trained on k-1 folds of training data and the remaining 1 fold will be used for validating the data. A mean and standard deviation metric will be generated to see how well the model will perform in practice.
I have dataset of gold prices and after modifying and some preprocessing i ended up with dataframe below:
There is 50,000 record in dataset and there are morethan 500 different markets with different frequencies, all columns expect date are int type and date is datetime object. i need to predict price per unit in some specific dates. but somehow i baffled with so many methods.
My question is what regression algorithm/method is results good prediction for this kind of data ?
In machine learning or data mining as they always say, a lot of things can be done in a lot of ways. Lets try to use elimination to decide on the algorithm for the given problem.The primary case is that the class variable (feature to be predicted) is continuous hence you should use any regression algorithms. I would suggest to go with linear regression, check the accuracy using r^2 score which is basically a squared difference between an actual and a predicted value. If it is not on par, try randomforest regressor.
This is my first brush with machine learning, so I'm trying to figure out how this all works. I have a dataset where I've compiled all the statistics of each player to play with my high school baseball team. I also have a list of all the players that have ever made it to the MLB from my high school. What I'd like to do is split the data into a training set and a test set, and then feed it to some algorithm in the scikit-learn package and predict the probability of making the MLB.
So I looked through a number of sources and found this cheat sheet that suggests I start with linear SVC.
So, then as I understand it I need to break my data into training samples where each row is a player and each column is a piece of data about the player (batting average, on base percentage, yada, yada), X_train; and a corresponding truth matrix of a single row per player that is simply 1 (played in MLB) or 0 (did not play in MLB), Y_train. From there, I just do Fit(X,Y) and then I can use predict(X_test) to see if it gets the right values for Y_test.
Does this seem a logical choice of algorithm, method, and application?
EDIT to provide more information:
The data is made of 20 features such as number of games played, number of hits, number of Home Runs, number of Strike Outs, etc. Most are basic counting statistics about the players career; a few are rates such as batting average.
I have about 10k total rows to work with, so I can split the data based on that; but I have no idea how to optimally split the data, given that <1% have made the MLB.
Alright, here are a few steps that might want to make:
Prepare your data set. In practice, you might want to scale the features, but we'll leave it out to make the first working model as simple as possible. So will just need to split the dataset into test/train set. You could shuffle the records manually and take the first X% of the examples as the train set, but there's already a function for it in scikit-learn library: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html. You might want to make sure that both: positive and negative examples are present in the train and test set. To do so, you can separate them before the test/train split to make sure that, say 70% of negative examples and 70% of positive examples go the training set.
Let's pick a simple classifier. I'll use logistic regression here: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html, but other classifiers have a similar API.
Creating the classifier and training it is easy:
clf = LogisticRegression()
clf.fit(X_train, y_train)
Now it's time to make our first predictions:
y_pred = clf.predict(X_test)
A very important part of the model is its evaluation. Using accuracy is not a good idea here: the number of positive examples is very small, so the model that unconditionally returns 0 can get a very high score. We can use the f1 score instead: http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html.
If you want to predict probabilities instead of labels, you can just use the predict_proba method of the classifier.
That's it. We have a working model! Of course, there are a lot thing you may try to improve, such as scaling the features, trying different classifiers, tuning their hyperparameters, but this should be enough to get started.
If you don't have a lot of experience in ML, in scikit learn you have classification algorithms (if the target of your dataset is a boolean or a categorical variable) or regression algorithms (if the target is a continuous variable).
If you have a classification problem, and your variables are in a very different scale a good starting point is a decision tree:
http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
The classifier is a Tree and you can see the decisions that are taking in the nodes.
After that you can use random forest, that is a group of decision trees that average results:
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
After that you can put the same scale in every feature:
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
And you can use other algorithms like SVMs.
For every algorithm you need a technique to select its parameters, for example cross validation:
https://en.wikipedia.org/wiki/Cross-validation_(statistics)
But a good course is the best option to learn. In coursera you can find several good courses like this:
https://www.coursera.org/learn/machine-learning
In simple words, what is the difference between cross-validation and grid search? How does grid search work? Should I do first a cross-validation and then a grid search?
Cross-validation is when you reserve part of your data to use in evaluating your model. There are different cross-validation methods. The simplest conceptually is to just take 70% (just making up a number here, it doesn't have to be 70%) of your data and use that for training, and then use the remaining 30% of the data to evaluate the model's performance. The reason you need different data for training and evaluating the model is to protect against overfitting. There are other (slightly more involved) cross-validation techniques, of course, like k-fold cross-validation, which often used in practice.
Grid search is a method to perform hyper-parameter optimisation, that is, it is a method to find the best combination of hyper-parameters (an example of an hyper-parameter is the learning rate of the optimiser), for a given model (e.g. a CNN) and test dataset. In this scenario, you have several models, each with a different combination of hyper-parameters. Each of these combinations of parameters, which correspond to a single model, can be said to lie on a point of a "grid". The goal is then to train each of these models and evaluate them e.g. using cross-validation. You then select the one that performed best.
To give a concrete example, if you're using a support vector machine, you could use different values for gamma and C. So, for example, you could have a grid with the following values for (gamma, C): (1, 1), (0.1, 1), (1, 10), (0.1, 10). It's a grid because it's like a product of [1, 0.1] for gamma and [1, 10] for C. Grid-search would basically train a SVM for each of these four pair of (gamma, C) values, then evaluate it using cross-validation, and select the one that did best.
Cross-validation is a method for robustly estimating test-set performance (generalization) of a model.
Grid-search is a way to select the best of a family of models, parametrized by a grid of parameters.
Here, by "model", I don't mean a trained instance, more the algorithms together with the parameters, such as SVC(C=1, kernel='poly').
Cross-validation, simply separating test and training data and validate training results with test data. There are two cross validation techniques that I know.
First, Test/Train cross validation. Splitting data as test and train.
Second, k-fold cross-validation split your data into k bins, use each bin as testing data and use rest of the data as training data and validate against testing data. Repeat the process k times. And Get the average performance. k-fold cross validation especially useful for small dataset since it maximizes both the test and training data.
Grid Search; systematically working through multiple combinations of parameter tunes, cross validate each and determine which one gives the best performance.You can work through many combination only changing parameters a bit.
Cross-validation is a method of reserving a particular subset of your dataset on which you do not train the model. Later, you test your model on this subset before finalizing it.
The main steps you need to perform to do cross-validation are:
Split the whole dataset in training and test datasets (e.g. 80% of the whole dataset is the training dataset and the remaining 20% is the test dataset)
Train the model using the training dataset
Test your model on the test dataset. If your model performs well on the test dataset, continue the training process
There are other cross-validation methods, for example
Leave-one-out cross-validation (LOOCV)
K-fold cross-validation
Stratified K-fold cross-validation
Adversarial cross-validation strategies (used when train and rest datasets are differ largely from each other).
In simple terms,
consider making pasta as building a model:
Cross validation - choosing the quantity of pasta
Grid search - choosing the right proportion of ingredients.