Machine learning in trading: theory, models, practice and algo-trading - page 488

[Andrey Kisselyov]

Maxim Dmitrievsky:

Well there is a question of the correct chips and targets, although it would seem what could be easier than the multiplication table, but there is not a small error

I can't be sure that you are learning correctly, and that's why there is no error.

I will never be able to check the correctness of your training, hence the mistakes.

[[Deleted]]

Andrey Kisselyov:
I can't be sure that you are learning correctly, and that's why there are errors.

I will never be able to check the correctness of your training, hence the mistakes.

Well, yes, considering that RF is not able to extrapolate at all

[[Deleted]]

Vizard_:

can...

(It's written everywhere that, like, no. )

[[Deleted]]

Vizard_:

You also wrote a rattle))). But you decided to make it produce another.
You want to set it up.
х = 1 0 1 0 1 0 1 0 1 0
target = 1 0 1 0 1 0 1 0 1 0 1 0
then -
х = 1 0 1 0 1 0 1 0 1 1
target = 1 0 1 0 1 0 1 0 1 0 1 0
etc...
On an interpretable example in short look.
accuracy,lloss, kappa...and p.r. whatever you like. Well and earlier rightly written-
there's a lot to see in the forest...

All right, if so, now I'll finish the strategy at once and then we'll see what's what :)

[Alexander Ivanov]

Greetings neuronists! Great minds ))

Here's a movie about a neuralist who created a super-predictive program and "helped" a bank get "rich".

[[Deleted]]

Alexander Ivanov:

Greetings neuronists! Great minds ))

here's a movie about a neuralist who created a super-predictive program and "helped" the bank "get rich" .

you should watch "The Texas Chainsaw Massacre", it's a new movie, it's relaxing.

[СанСаныч Фоменко]

I can't help thinking that a number of problems are common to both classification and regression models.

One such problem is multicollinearity, which is usually interpreted as a correlation between input variables, but this may not be entirely true.

Multicollinearity in common parlance leads to extremely unpleasant consequences that negate our modeling efforts:

• model parameters become uncertain
• standard estimation errors become infinitely large

If multicollinearity is understood as a linear relationship between input variables (explanatory variables, predictors), then we have the following picture

• Although the OLS estimates are still unbiased, they have large variance and covariance, making an accurate estimate difficult
• As a result, the confidence intervals tend to be wider. Therefore, we may not reject the "null hypothesis" (i.e., the true sampling ratio is zero),
• Because of the first effect of t the ratios of one or more coefficients tend to be statistically insignificant
• Even though some regression coefficients are statistically insignificant, the R^2 value can be very high
• OLS estimation tools and their standard errors can be sensitive to small changes in the data

Here is an article that provides R tools to recognize the presence of multicollinearity.

[[Deleted]]

SanSanych Fomenko:

I can't help thinking that a number of problems are common to both classification and regression models.

One such problem is multicollinearity, which is usually interpreted as a correlation between input variables, but this may not be entirely true.

Multicollinearity in common parlance leads to extremely unpleasant consequences that negate our modeling efforts:

• model parameters become uncertain
• standard estimation errors become infinitely large

If multicollinearity is understood as a linear relationship between input variables (explanatory variables, predictors), then we have the following picture

• Although the OLS estimates are still unbiased, they have large variance and covariance, making an accurate estimate difficult
• As a result, the confidence intervals tend to be wider. Therefore, we may not reject the "null hypothesis" (i.e., the true sampling ratio is zero),
• Because of the first effect of t, the ratios of one or more coefficients tend to be statistically insignificant
• Even though some regression coefficients are statistically insignificant, the R^2 value can be very high
• OLS estimation tools and their standard errors can be sensitive to small changes in the data

Here is an article that provides R tools to recognize the presence of multicollinearity.

thanks for the new word, already had a couple of glosses today :)

what other problems are there?

[forexman77]

Today I decided to check my network based on Percetron. Optimized to May-early June 2016, EURUSD, spread 15 pips.

The tail itself.

I am still confused by the result.

[Yuriy Asaulenko]

forexman77:

Today I decided to check my network based on Percetron. Optimized to May-early June 2016, EURUSD, spread 15 pips.

The tail itself.

So far I am confused by the result.

I am spoiled too, even somewhere in a kind of shock. I have tried it with random samples and the results are amazing. I have not done TC yet.

Maxim says it takes a long time to learn. I have about 23 hours. But even if I do it once every 3 months - what a rubbish).

The difference is that I should have had it for 3 months.