I am using the Google business api, suddenly the entire api request stopped, the graph shows this:
REDUCE_PERCENTILE_99
Any idea what does this means please?
Thank you.
After research, I found that a Reducer operation describes how to aggregate data points from multiple time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.
REDUCE_PERCENTILE_99: Reduce by computing the 99th percentile of data points across time series for each alignment period. This reducer is valid for GAUGE and DELTA metrics of numeric and distribution type. The value of the output is DOUBLE.
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.
I have moderate experience with data science. I have a data set with 9500 observations and more than 4500 features most of which are highly correlated. Here is briefly what I have tried: I have dropped columns where there are less than 6000 non-NAs and have imputed NAs with their corresponding columns' median values when there are at least 6000 non-NAs. As for correlation, I have kept only features having at most 0.7 correlation with others. By doing so, I have reduced the number of features to about 750. Then I have used those features in my binary classification task in random forest.
My data set is highly unbalanced where ratio of (0:1) is (10:1). So when I apply RF with 10-fold cv, I observe too good results in each cv (AUC of 99%) which is to good to be true and in my test set I got way worse results such as 0.7. Here is my code:
import h2o
from h2o.estimators import H2ORandomForestEstimator
h2o.init(port=23, nthreads=4)
train = fs_rf[fs_rf['Year'] <= '201705']
test = fs_rf[fs_rf['Year'] > '201705']
train = train.drop('Year',axis=1)
test = test.drop('Year',axis=1)
test.head()
train = h2o.H2OFrame(train)
train['BestWorst2'] = train['BestWorst2'].asfactor()
test = h2o.H2OFrame(test)
test['BestWorst2'] = test['BestWorst2'].asfactor()
training_columns = train.drop('BestWorst2',axis=1).col_names
response_column = 'BestWorst2'
model = H2ORandomForestEstimator(ntrees=100, max_depth=20, nfolds=10, balance_classes=True)
model.train(x=training_columns, y=response_column, training_frame=train)
performance = model.model_performance(test_data=test)
print(performance)
How could I avoid this over-fitting? I have tried many different parameters in grid search but none of them improved the results.
This is not what I would call "overfitting". The reason you are seeing really good cross-validation metrics compared to your test metrics is that you have time-series data and so you can't use k-fold cross-validation to give you an accurate estimate of performance.
Performing k-fold cross-validation on a time-series dataset will give you overly-optimistic performance metrics because you are not respecting the time-series component in your data. Regular k-fold cross-validation will randomly sample from your whole dataset to create a train & validation set. Essentially, your validation strategy is "cheating" because you have "future" data included in your CV training sets (if that makes any sense).
I can see by your code that you understand that you need to train with "past" data and predict on "future" data, but if you want to read more about this topic, I'd recommend this article or this article.
One solution is to simply look at test set performance as way to evaluate your model. Another option is to use what's called "rolling" or "time-series" cross-validation, but H2O does not currently support that (though it seems like it might be added soon). Here's a ticket for this if you want to keep track of the progress.
The sum of the incorrect classification (see tree) in all rules is 2097 (which is from 895+700+428+74) . But the confusion matrix is 2121 (which is from 1999+122). Can someone explain the discrepancy? How come the numbers are different?
Weka output of classifier's model description contains two sections
Error on training data
Stratified cross-validation
First one just evaluate trained classifier on training data itself whereas second one does the cross-validation where it distribute instances of each class equally in each fold. So stratified cross-validation is supposed to produce better picture of classifier's performance as compared to simple cross-validation.
I think here you have posted Confusion matrix of stratified cross-validation & hence number of miss-classified instances shown in tree(They must be from evaluation on training data) is different.
Decision tree output is very nicely described at link https://weka.wikispaces.com/Primer#classifiers. There also miss-classified examples shown in tree are different from those that can be seen from confusion matrix under stratified cross-validation section.
Hope, I am correct.
I've been doing some work for my exams in a few days and I'm going through some past papers but unfortunately there are no corresponding answers. I've answered the question and I was wondering if someone could tell me if I am correct.
My question is
(c) A transactional dataset, T, is given below:
t1: Milk, Chicken, Beer
t2: Chicken, Cheese
t3: Cheese, Boots
t4: Cheese, Chicken, Beer,
t5: Chicken, Beer, Clothes, Cheese, Milk
t6: Clothes, Beer, Milk
t7: Beer, Milk, Clothes
Assume that minimum support is 0.5 (minsup = 0.5).
(i) Find all frequent itemsets.
Here is how I worked it out:
Item : Amount
Milk : 4
Chicken : 4
Beer : 5
Cheese : 4
Boots : 1
Clothes : 3
Now because the minsup is 0.5 you eliminate boots and clothes and make a combo of the remaining giving:
{items} : Amount
{Milk, Chicken} : 2
{Milk, Beer} : 4
{Milk, Cheese} : 1
{Chicken, Beer} : 3
{Chicken, Cheese} : 3
{Beer, Cheese} : 2
Which leaves milk and beer as the only frequent item set then as it is the only one above the minsup?
I agree you should go for the Apriori Algorithm.
The Apriori algorithm is based on the idea that for a pair o items to be frequent, each individual item should also be frequent.
If the hamburguer-ketchup pair is frequent, the hamburger itself must also appear frequently in the baskets. The same can be said about the ketchup.
So for the algorithm, it is established a "threshold X" to define what is or it is not frequent. If an item appears more than X times, it is considered frequent.
The first step of the algorithm is to pass for each item in each basket, and calculate their frequency (count how many time it appears).
This can be done with a hash of size N, where the position y of the hash, refers to the frequency of Y.
If item y has a frequency greater than X, it is said to be frequent.
In the second step of the algorithm, we iterate through the items again, computing the frequency of pairs in the baskets. The catch is that
we compute only for items that are individually frequent. So if item y and item z are frequent on itselves,
we then compute the frequency of the pair. This condition greatly reduces the pairs to compute, and the amount of memory taken.
Once this is calculated, the frequencies greater than the threshold are said frequent itemset.
(http://girlincomputerscience.blogspot.com.br/2013/01/frequent-itemset-problem-for-mapreduce.html)
There are two ways to solve the problem:
using Apriori algorithm
Using FP counting
Assuming that you are using Apriori, the answer you got is correct.
The algorithm is simple:
First you count frequent 1-item sets and exclude the item-sets below minimum support.
Then count frequent 2-item sets by combining frequent items from previous iteration and exclude the item-sets below support threshold.
The algorithm can go on until no item-sets are greater than threshold.
In the problem given to you, you only get 1 set of 2 items greater than threshold so you can't move further.
There is a solved example of further steps on Wikipedia here.
You can refer "Data Mining Concepts and Techniques" by Han and Kamber for more examples.
OK to start, you must first understand, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Now, the amount of raw data stored in corporate databases is exploding. From trillions of point-of-sale transactions and credit card purchases to pixel-by-pixel images of galaxies, databases are now measured in gigabytes and terabytes. (One terabyte = one trillion bytes. A terabyte is equivalent to about 2 million books!) For instance, every day, Wal-Mart uploads 20 million point-of-sale transactions to an A&T massively parallel system with 483 processors running a centralized database. Raw data by itself, however, does not provide much information. In today's fiercely competitive business environment, companies need to rapidly turn these terabytes of raw data into significant insights into their customers and markets to guide their marketing, investment, and management strategies.
Now you must understand that association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule. Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in real- life applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. This dilemma is called the rare item problem. This paper proposes a novel technique to solve this problem. The technique allows the user to specify multiple minimum supports to reflect the natures of the items and their varied frequencies in the database. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules.
Given a set of transactions T (the database), the problem of mining association rules is to discover all association rules that have support and confidence greater than the user-specified minimum support (called minsup) and minimum confidence (called minconf).
I hope that once you understand the very basics of data mining that the answer to this question shall become apparent.