I am using PCA from sklearn for data reduction with 27 features and 3558 rows data and I got the following result
I have read a lot of articles but they just explain the correlation between variables and each principal component (loadings), but here I want to know what does negative and positive value means in each cell (PC1 PC2 PC3) in each data row in the final result?
You can't really deduce much from the sign of a value in the principal components. Remember that the principal components are linear combinations of initial features. Thus, a negative component just means that a linear combination gave a negative number. For example you could have the first component equal to 5*feature_1-7*feature_2=x where x<0.
Not sure what you are trying to achieve here but if you want to understand more deeply what those components mean, I suggest to look at the code at the very bottom of this page.
Negative value means the component is not significant.
Positive value means the component is significant.
Related
Image of the problem (https://i.stack.imgur.com/Ie0ZV.png)
The IF formula is supposed to give the answer yes if the two values are the same, which they are. Although I get the answer No. Why is this happening?
The two values are themselves formulas from other sheets.
Image of attempt to solve (https://i.stack.imgur.com/qetL3.png)
I tried to use numbers directly put in the same sheet (no numbers coming from a formula), and then it works. But I need to use values that are based on formulas taking numbers from other sheets.
while the values derived from the formulas is just showcasing 2 decimal places, its likely that when you expand/increase the decimals to extreme end there could be a possible difference (alike the one you can see in the screenshot for reference; the EXACT() in Cell B3 throws 'FALSE')
You could ROUND() them up for exact match scenario.
I do understand the principle component analysis. I know how to do it and what it actually does. I have applied PCA and my best result has shown to be two components. I do understand that each of my inputs are now contributing partially in each component. What I do not understand is how to feed the result of PCA (in my case 2 components ) to a machine learning model?
How do we input them?
For example when I want to run a NN on my features, I just can navigate to where they are stored and import them, but my PCA analysis has been run in SPSS and all it shows me is the contribution of my features on each component.
What should I import to my NN model?
PCA is a method of feature extraction, which is used to avoid the problem of co-linearity. For example, if several variables are highly correlated because "they measure the same thing", then PCA can extract a measure of "that thing" (technically: a component), which is called a score. Your data set of, say, 100 measured variables may reduce to, say, 10 significant components. Then you can use the scores your test persons have achieved in those 10 components to do for example a multi-dimensional regression, a cluster analysis or a discriminance analysis. This will result in more valid results than performing the analysis directly on the 100 variables.
So the procedure is to sort the eigenvalues (and -vectors) by size, identify the number of significant components p (e.g., by scree-plot), set up the projection matrix F (eigenvectors corresponding to the largest q eigenvalues in columns) and multiply it with the data matrix D. This will give you the score matrix C (dimension n times q, with n the number of test persons), which you can use as input for whatever method you want to use next.
Hi I am using a random forest classifier to product logerror. The log error contains both =ve & -ve values. After running the classifier with different settings. i am able to get training test score of around 0.8 but the test score is always negative. why is that so?
should i be using abs(log error) for prediction or is my choice for random forest wrong?
Choice of the random forest might be wrong but you better check it in context of data as if you have shared data here it would be easy to help you at the exact point. But, I suggest you try Knn if your total observations are around 1000-2000.
Also, if you are using any kind of encoding to convert categorical data to nominal please use only one hot encoding as other my add values to attributes.
You should have checked correlation of attributes to the target variable as the low correlation of target variable in test data may result in the negative score.
Apart from above all, distribution of data plays a vital role in randomforest regresssion. So, try to check distribution and apply methods such as box-cox to convert data in normal distribution.
I am working user behavior project. Based on user interaction I have got some data. There is nice sequence which smoothly increases and decreases over the time. But there are little discrepancies, which are very bad. Please refer to graph below:
You can also find data here:
2.0789 2.09604 2.11472 2.13414 2.15609 2.17776 2.2021 2.22722 2.25019 2.27304 2.29724 2.31991 2.34285 2.36569 2.38682 2.40634 2.42068 2.43947 2.45099 2.46564 2.48385 2.49747 2.49031 2.51458 2.5149 2.52632 2.54689 2.56077 2.57821 2.57877 2.59104 2.57625 2.55987 2.5694 2.56244 2.56599 2.54696 2.52479 2.50345 2.48306 2.50934 2.4512 2.43586 2.40664 2.38721 2.3816 2.36415 2.33408 2.31225 2.28801 2.26583 2.24054 2.2135 2.19678 2.16366 2.13945 2.11102 2.08389 2.05533 2.02899 2.00373 1.9752 1.94862 1.91982 1.89125 1.86307 1.83539 1.80641 1.77946 1.75333 1.72765 1.70417 1.68106 1.65971 1.64032 1.62386 1.6034 1.5829 1.56022 1.54167 1.53141 1.52329 1.51128 1.52125 1.51127 1.50753 1.51494 1.51777 1.55563 1.56948 1.57866 1.60095 1.61939 1.64399 1.67643 1.70784 1.74259 1.7815 1.81939 1.84942 1.87731
1.89895 1.91676 1.92987
I would want to smooth out this sequence. The technique should be able to eliminate numbers with characteristic of X and Y, i.e. error in mono-increasing or mono-decreasing.
If not eliminate, technique should be able to shift them so that series is not affected by errors.
What I have tried and failed:
I tried to test difference between values. In some special cases it works, but for sequence as presented in this the distance between numbers is not such that I can cut out errors
I tried applying a counter, which is some X, then only change is accepted otherwise point is mapped to previous point only. Here I have great trouble deciding on value of X, because this is based on user-interaction, I am not really controller of it. If user interaction is such that its plot would be a zigzag pattern, I am ending up with 'no user movement data detected at all' situation.
Please share the techniques that you are aware of.
PS: Data made available in this example is a particular case. There is no typical pattern in which numbers are going to occure, but we expect some range to be continuous with all the examples. Solution I am seeking is generic.
I do not know how much effort you want to involve in this problem but if you want theoretical guaranties,
topological persistence seems well adapted to your problem imho.
Basically with that method, you can filtrate local maximum/minimum by fixing a scale
and there are theoritical proofs that says that if you sampling is
close from your function, then you extracts correct number of maximums with persistence.
You can see these slides (mainly pages 7-9 to get the idea) to get an idea of the method.
Basically, if you take your points as a landscape and imagine a watershed starting from maximum height and decreasing, you have some picks.
Every pick has a time where it is born which is the time where it becomes emerged and a time where it dies which is when it merges with an higher pick. Now a persistence diagram pictures a point for every pick where its x/y coordinates are its time of birth/death (by assumption the first pick does not die and is not shown).
If a pick is a global maximal, then it will be further from the diagonal in the persistence diagram than a local maximum pick. To remove local maximums you have to remove picks close to the diagonal. There are fours local maximums in your example as you can see with the persistence diagram of your data (thanks for providing the data btw) and two global ones (the first pick is not pictured in a persistence diagram):
If you noise your data like that :
You will still get a very decent persistence diagram that will allow you to filter local maximum as you want :
Please ask if you want more details or references.
Since you can not decide on a cut off frequency, and not even on the filter you want to use, I would implement several, and let the user set the parameters.
The first thing that I thought of is running average, and you can see that there are so many things to set, to get different outputs.
I am new in rapid miner 5, just want to know how to find noise in my data and show them in chart and how to delete them?
A complex problem because it depends what you mean by noise.
If you mean finding individual attributes whose values are plain wrong then you could plot a histogram view and work out some sort of limits on what constitutes a valid value. You could then impose that rule by using Filter Examples to remove them.
If you mean finding attributes that have some sort of random jitter applied to them it would be difficult to detect these. Only by knowing beforehand what the expected shape of the distribution is could you compare with observation and do something about it. However, the action to take is by no means obvious.
If you mean finding examples within an example set that are obviously different from other examples then you could consider using the various outlier functions. The simplest one to get started is Detect Outlier (Distances). This finds a set number of outliers (default 10) based on a distance calculation that uses all the attributes for examples. It creates a new attribute called outlier that is set to true or false. You could then use the Filter Examples operator to remove those that are set to true.
Hope that helps at least as a start.