I am new to data mining concepts and have a question regarding implementation of a technique.
I am using the a dataset with large continuous values.
Now, I am trying to code an algorithm where I need to discretize data (not scale as it makes no impact on data along with the fact that algorithm is not a distance based one, hence no scaling needed).
Now for discretization, I have a similar question with regards to scaling and train test split.
For scaling, I know we should split data and then fit transform the train and transform the test based on what we fit from train.
But what do we do for discretization? I am using scikit learns KBinsDiscretizer and trying to make sense of whether I should split first and discretize the same way we normally scale or discretize first then scale.
The issue came up because I used the 17 bins, uniform strategy (0-16 value range)
With split then discretize, I get (0-16) range throughout in train but not in test.
With discretize and split, I get (0-16) range in both.
With former strategy, my accuracy is around 85% but with the latter, its a whopping 97% which leads me to believe I have definitely overfit the data.
Please advise on what I should be doing for discretization and whether the data interpretation was correct.
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I read somewhere suggesting that in case there are multiple features(multi linear model) no feature scaling is needed because co-efficient takes care of that.
But for single feature(simple linear model); feature scaling is needed.
Is this how python scikilt learn works or I read something wrong?
Need answer from someone who has tested both with and without feature scaling in simple linear regression
Scaling is used when we want to scale the features in a particular range. In particular algorithms, the model will be sensitive to outliers so it is recommended to scale the features in a particular range. Algorithms like distance-based need feature scale. It also depends on data not in particular for any dataset such as multiple linear regression or linear regression. Sometimes features scaling is not recommended as the data points will shift from a particular range to a normal distribution range as it will lead to an impact on modelling.
i am new to datascience and when i was going through one of the kaggle blog, i saw that the user is using both scale and fit on the data set. i tried to understand the difference by going through the documentation but was not able to understand
It's hard to understand the source of your confusion without any code. Inside the link you provided, the data is first scaled with sklearn.preprocessing.scale() and then fit to a sklearn.ensemble.GradientBoostingRegressor.
So the scaling operation transforms data such that all the features are represented on the same scale, and the fitting operation trains the model with the said data.
From your question it sounds like you thought these two operations were mutually exclusive, or somehow equivalent, but they are actually logical consecutive steps.
In general, before model is trained, data is somehow preprocessed (with .scale() in this case), then trained. In sklearn the .fit() methods are for training (fitting functions/models to the data).
Hope it makes sense!
Scale is a data normalization technique and it is used when data in different features are of not similar values like in one feature you have values ranging from 1 to 10 and in other features you have values ranging from 1000 to 10000.
Where as fit is the function that actually starts your model training
Scaling is conversion of data, a method used to normalize the range of independent variables or features of data. The fit method is a training step.
I would like to use scikit-learn's svm.SVC() estimator to perform classification tasks on multi-dimensional time series - that is, on time series where the points in the series take values in R^d, where d > 1.
The issue with doing this is that svm.SVC() will only take ndarray objects of dimension at most 2, whereas the dimension of such a dataset would be 3. Specifically, the shape of a given dataset would be (n_samples, n_features, d).
Is there a workaround available? One simple solution would just be to reshape the dataset so that it is 2-dimensional, however I imagine this would lead to the classifier not learning from the dataset properly.
Without any further knowledge about the data reshaping is the best you can do. Feature engineering is a very manual art that depends heavily on domain knowledge.
As a rule of thumb: if you don't really know anything about the data throw in the raw data and see if it works. If you have an idea what properties of the data may be beneficial for classification, try to work it in a feature.
Say we want to classify swiping patterns on a touch screen. This closely resembles your data: We acquired many time series of such patterns by recording the 2D position every few milliseconds.
In the raw data, each time series is characterized by n_timepoints * 2 features. We can use that directly for classification. If we have additional knowledge we can use that to create additional/alternative features.
Let's assume we want to distinguish between zig-zag and wavy patterns. In that case smoothness (however that is defined) may be a very informative feature that we can add as a further column to the raw data.
On the other hand, if we want to distinguish between slow and fast patterns, the instantaneous velocity may be a good feature. However, the velocity can be computed as a simple difference along the time axis. Even linear classifiers can model this easily so it may turn out that such features, although good in principle, do not improve classification of raw data.
If you have lots and lots and lots and lots of data (say an internet full of good examples) Deep Learning neural networks can automatically learn features to some extent, but let's say this is rather advanced. In the end, most practical applications come down to try and error. See what features you can come up with and try them out in practice. And beware the overfitting gremlin.
I am working on a project where I use Spark Mllib Linear SVM to classify some data (l2 regularization). I have like 200 positive observation, and 150 (generated) negative observation, each with 744 features, which represent the level of activity of a person in different region of a house.
I have run some tests and the "areaUnderROC" metric was 0.991 and it seems that the model is quite good in classify the data that I provide to it.
I did some research and I found that the linear SVM is good in high dimensional data, but the problem is that I don't understand how something linear can divide my data so well.
I think in 2D, and maybe this is the problem but looking at the bottom image, I am 90% sure that my data looks more like a non linear problem
So it is normal that I have good results on the tests? Am I doing something wrong? Should I change the approach?
I think you question is about 'why linear SVM could classfy my hight Dimensions data well even the data should be non-linear'
some data set look like non-linear in low dimension just like you example image on right, but it is literally hard to say the data set is definitely non-linear in high dimension because a nD non-linear may be linear in (n+1)D space.So i dont know why you are 90% sure your data set is non-linear even it is a high Dimension one.
At the end, I think it is normal that you have a good test result in test samples, because it indicates that your data set just is linear or near linear in high Dimension or it wont work so well.Maybe cross-validation could help you comfirm that your approach is suitable or not.
I am working on a project to classify hearing disorders using SVM. I have collected real time data from the site (http://archive.ics.uci.edu/ml/machine-learning-databases/audiology/) and initially decided to go for two classes to classify patients with normal ear and patients with any disorder. Varying the optimization parameter C from 0.1 to 10 I get one miss-classification between the two classes (C=10).
However I wan to plot the data with the decision boundary but the data set has around 68 features so it is not possible to plot it. I used PCA to reduce to 2D and used svm on this data to see the results. But when I use PCA, the data no longer remains linearly separable and linear decision boundary cannot separate the 2D PCA data. So I want to know if there is a way to reduce dimension but to retain the nature of the data (nature as in separability using linear decision boundary). Can anyone please help me?
Thanks