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What is random seed in Weka?
Seed is just a value by which you can fix the Random Numbers that are being generated in your task. Also, this is a general concept and not just for weka. So, here random numbers are being used to split the data. Now if you run the code without fixing any seed, you will get different splits on every run.
What do you mean by seed value?
A seed value specifies a particular stream from a set of possible random number streams. When you specify a seed, SAS generates the same set of pseudorandom numbers every time you run the program.
What is random seed in machine learning?
A random seed is a starting point in generating random numbers. A random seed specifies the start point when a computer generates a random number sequence. This can be any number, but it usually comes from seconds on a computer system’s clock (Henkemans & Lee, 2001).
Why is random seed important?
❓ What is a Random Seed? A random seed is used to ensure that results are reproducible. In other words, using this parameter makes sure that anyone who re-runs your code will get the exact same outputs. In addition to reproducibility, random seeds are also important for bench-marking results.
What is seed in Kmeans?
The seed number (any integer) is the randomization for your initial K points. K represents the number of clusters. Because Kmeans is sensitive to initial points, you will have to try experimentation on the stability of your clusters with different seeds.
What is seed in K means clustering?
Clustering is one of the important unsupervised learning in data mining to group the similar features. The growing point of the cluster is known as a seed. To select the appropriate seed of a cluster is an important criterion of any seed based clustering technique.
What is a seed in coding?
A random seed (or seed state, or just seed) is a number (or vector) used to initialize a pseudorandom number generator. Random seeds are often generated from the state of the computer system (such as the time), a cryptographically secure pseudorandom number generator or from a hardware random number generator.
Why do we use random state?
Random state ensures that the splits that you generate are reproducible. Scikit-learn uses random permutations to generate the splits. The random state that you provide is used as a seed to the random number generator. This ensures that the random numbers are generated in the same order.
How do you use random seeds?
Typically you just invoke random. seed() , and it uses the current time as the seed value, which means whenever you run the script you will get a different sequence of values. The seed is what is fed to the RNG to generate the first random number. After that, they RNG is self-fed.
Is K-means sensitive to starting seeds?
The number of initial seeds (initial centers of clusters) is the same as number of clusters (at leats in the original k-means). The problem of the VALUES of the seeds is different than problem of number of clusters… normally you would use random cluster centers, but some research points to better ways to choose them.
What does the seed value mean in Weka?
The weka SimpleKMeans implementation allows the user to specify a “seed value” with the option -s. I do not understand what it signifies. In this link, Mark Hall, the weka architect, says that it is supposed to generate random numbers.
How is the seed value used in k-means?
The seed value is used in generating a random number which is, in turn, used for making the initial assignment of instances to clusters. Note that, in general, K-means is quite sensitive to how clusters are initially assigned. Thus, it is often necessary to try different values and evaluate the results.
What does k mean in Weka k clustering?
So the randomness is commonly involved with choosing the initial centers. K-means++ is an alternate way of choosing initial seeds that fortunately is still randomized (there are some that are not randomized, so you can no longer try to improve your results with multiple runs then), but tries to choose a better starting situation.
Do you need filters for clustering in Weka?
While WEKA provides filters to accomplish all of these preprocessing tasks, they are not necessary for clustering in WEKA . This is because WEKA SimpleKMeans algorithm automatically handles a mixture of categorical and numerical attributes. Furthermore, the algorithm automatically normalizes numerical attributes when doing distance computations.