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Simple example using House Pricing Scenario #53
Description
This is the first time I've looked into machine learning but I have a use case I'd like to test with it.
To get started I've created a simple example from the house pricing scenario which somewhat closely matches my use case but the results I'm getting are not at all close to what I expected. The data I'm providing is simply linear in terms of just the SqftLiving input parameter to the Price where Price = SqftLiving * 100. The SqftLot is held constant for training and prediction so it should be a non-factor.
I'm just trying to predict the Price when the SqftLiving is 1500 which with the linear model created by the provided data should make it about $150,000.
However, the results I get vary wildly from the negative to the postive 10's of millions every time I run the program which is unexpected. Could someone look into this simple example and let me know what if anything I'm doing is causing these poor results?
class Program
{
static void Main(string[] args)
{
var filePath = "C://Temp/kc_house_data.csv";
File.WriteAllText(filePath, @"100000,1000,8000
200000,2000,8000
400000,4000,8000");
var pipeline = new LearningPipeline
{
new TextLoader<HousePriceData>(filePath, separator: ","),
new ColumnConcatenator("Features", "SqftLiving", "SqftLot"),
new StochasticDualCoordinateAscentRegressor()
};
var model = pipeline.Train<HousePriceData, HousePricePrediction>();
var prediction = model.Predict(new HousePriceData { SqftLiving = 1500, SqftLot = 8000 });
Console.WriteLine(prediction.Price);
Console.ReadLine();
}
}
public class HousePriceData
{
[Column(ordinal: "0", name: "Label")]
public float Price;
[Column(ordinal: "1")]
public float SqftLiving;
[Column(ordinal: "2")]
public float SqftLot;
}
public class HousePricePrediction
{
[ColumnName("Score")]
public float Price;
}
Activity
Ivanidzo4ka commentedon May 7, 2018
Hello Tyler.
Let me point on the fact what you have only 3 data points, and our algorithms expected to be worked on millions of data points, with huge feature vector. For you as a person. it's obvious what formula states behind this 3 points, but from machine point of view, 3 points is not enough.
I've made following change in your code:
And I played with different values for n.
n=100 gives me prediction for your example around 141 000 which is about 6% error.
n=1000 gives me prediction 14910 which 0.6% error
n=100000 gives me 14992 which is 0.06% error
So the more data you give to algorithm, the better it performs.
You can also play with different learning models. For example if I modify my generating code to make
var sqft = random.NextDouble() *10_000;to reflect real values for square foots I can use FastTreeRegressor, and it will give me reasonable prediction (2% error) even with 100 examples.
TylerBrinkley commentedon May 7, 2018
@Ivanidzo4ka Thanks for the insight and the follow up with more data point results. I look forward to playing with this some more where I'll have a much larger data set.
Revert "Set Nullable Auto params to null values" (dotnet#53)
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Revert "Set Nullable Auto params to null values" (dotnet#53)
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