svm - Machine Learning Algorithm Confusion -


I have made a small application about cricket prediction using machine learning. I took a record of 10 years (2001-2011) of ODI matches and prepared a training set.

Now to predict victory or loss for a particular team, I have considered several factors.

For example, this is the India vs Australia match at Wankhede stadium, India.

  1. India's record in the last 10 years.

  2. India's record in the last two years.

  3. India has a record of the last two years in India.

  4. India's record in Wankhede in the last two years,

  5. India's record in Wankhede.

  6. India's record in Wankhede in the last two years.

  7. Australia's record in the last two years.

  8. Australia's record in the last 10 years.

  9. Australia's record in the last two years.

  10. Australia's record against India in the last 10 years.

  11. Australia's record against India in the last two years.

  12. Australia's record against India in the last 10 years in India.

  13. Australia's record against India in the last two years in India.

So we took all the possibilities, for example, India played in 322 games in 10 years and won 140, so the probability of winning is 140/322 and the other For all the factors, we finally added all the possibilities and found the loss percentage of winning for both countries. I wanted to know what kind of the theorem is. It started as a stupid twenty two, but in Nice Bayes we multiply the possibilities, unlike here. You can check the implementation here, we have used basic PHP so that we can find potential prospects using SQL queries. Now there may be a wrong view to go about this amount, alternative ways are welcome.

This is a trivial linear model, where you are not fit even in the weight of the model, but rather stable Use the values ​​linear model

  cl (x) = sgn ( w, x & gt; + b) = sgn (sUM w_i x_i + b)  

Where X is your data point (x_i ith is the attribute). In your case, all w_i = 1 (you just add all the features, that's all). Calling this "theorem" will be very high, it is considered to be only a priority (as you do not train it) trivial (as the constant value, no expert knowledge) linear model (as it is the use of weighted totals of these characteristics Does). / P>


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