Our Over and Under Sampling can combat that. In both the left and right side of the image above, our blue class has far more samples than the orange class. In this case, we have 2 pre-processing options which can help in the training of our Machine Learning models. Undersampling means we will select only some of the data from the majority class, only using as many examples as the minority class has.
This selection should be done to maintain the probability distribution of the class. That was easy!
We just evened out our dataset by just taking less samples! Oversampling means that we will create copies of our minority class in order to have the same number of examples as the majority class has. The copies will be made such that the distribution of the minority class is maintained. We just evened out our dataset without getting any more data! Fully understanding why we use Bayesian Statistics requires us to first understand where Frequency Statistics fails. It involves applying math to analyze the probability of some event occurring, where specifically the only data we compute on is prior data.
Suppose I gave you a die and asked you what were the chances of you rolling a 6. Indeed if we were to do a frequency analysis we would look at some data where someone rolled a die 10, times and compute the frequency of each number rolled; it would roughly come out to 1 in 6! But what if someone were to tell you that the specific die that was given to you was loaded to always land on 6? Since frequency analysis only takes into account prior data , that evidence that was given to you about the die being loaded is not being taken into account.
Bayesian Statistics does take into account this evidence.
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The probability P H in our equation is basically our frequency analysis; given our prior data what is the probability of our event occurring. The P E H in our equation is called the likelihood and is essentially the probability that our evidence is correct, given the information from our frequency analysis. The P E is the probability that the actual evidence is true. As you can see from the layout of the equation Bayesian statistics takes everything into account.
Use it whenever you feel that your prior data will not be a good representation of your future data and results. Connect with me on LinkedIn too! Want to learn more about Data Science?
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Structures should then be exported into a dpi RGB tiff file before being submitted. Stereo diagrams should be presented for divergent 'wall-eyed' viewing, with the two panels separated by 5. In the final accepted version of the manuscript, the stereo images should be submitted at their final page size. It should be clear what statistical test was used to generate every P value.
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Use of the word "significant" should always be accompanied by a P value; otherwise, use "substantial," "considerable," etc. Data sets should be summarized with descriptive statistics, which should include the n value for each data set, a clearly labelled measure of centre such as the mean or the median , and a clearly labelled measure of variability such as standard deviation or range. Ranges are more appropriate than standard deviations or standard errors for small data sets. Graphs should include clearly labelled error bars.
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