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If you use a ML model to learn a score that you built based on your features the best thing that could happen is that your model learn exactly the rules you applied on your features. It would not be usefull as it would be equivalent to applying your rules in the first place. Supervised ML is only usefull to learn unknown rules. Basically you know have two ...

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I would recommend you to check Student's t-test .The formula is t=(m1-m2)/math.sqrt(s1^2/n1+s2^2/n2) where m is mean of the sample,s is standart deviation and n is the size.

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Of course you can, the t-test includes $n_1$ and $n_2$ in the equations. For $H_0:\mu_1=\mu_2$ and $H_1:\mu_1\neq\mu_2$ we have the test stat: $T = \frac{\bar{Y_1}-\bar{Y_2}}{s_\text{pooled}\sqrt{s_1^2/n_1 + s_2^2/n_2}}$ where $s_\text{pooled} = \frac{(n_1-1)s_1^2+(n_2-1)s_2^2}{n_1+n_2-2}$ Just plug in your values and compare it to the t distribution with $\... 2 Generally speaking, you should investigate the process by which your values are missing and try to deal with it. I assume you checked that : There is no meaningfull way to fill those missing values. Sometimes, typically with companies data that often represent amount of money, missing values means 0$. For exemple values missing by block may correspond to a ...

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I agree that the main potential issue is bias due to a particular group of instances being over-represented in the missing values. For example it's possible that the type of company is missing in cases where it's unknown or ambiguous, and this might correspond to a particular company profile (e.g. smaller, more recent, ...). To me the measures you propose to ...

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When you look at a linear regression $y_i = \beta_0 + \beta_1 x_i + u_i$, you can estimate the (ex ante unknown) coefficients $\beta$ using matrix algebra $(X'X)^{-1} X'y = \hat{\beta}$. A point estimate would be the "best guess" $\hat{y}=\hat{\beta} X$. Each $\hat{\beta}$ is associated with uncertainty about the estimate, expressed by the standard ...

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If you want the probability of different classes as the output, you should use SOFTMAX activation function. It outputs probability of all the classes based on the above formula.

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If what you want is to generate random numbers with the same distribution as your cashflow numbers I recommend you using Python's Fitter package It is powerful and very simple to use. You can in this way use it to find the distribution of your data and then generate random numbers with the same distribution. From documentation: from scipy import stats data = ...

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One possible solution is Bayesian linear regression. Bayesian linear regression estimates a posterior distribution for each coefficient. From that posterior distribution, a credible interval can be calculated.

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No - AUC (Area Under Curve) can not be used directly to assess the performance of multi-class classification. If you want to use AUC, it is necessary to binarize the output. Either each class as to be compared against each other class or 1 class versus the rest.

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Let's say you want to learn a specific mapping from $\mathbb{R}^{n}$ to $\mathbb{R}^{m}$. The following elaboration assumes that you are referring to supervised learning. Hyperplane Hyperplanes play a key role in neural networks. Consider the set $H_{v,d} := \{x \in \mathbb{R}^{n} \mid \langle x,v \rangle = d \}$ for $v \in \mathbb{R}^{n}$ and \$d \in \...

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