logistic | Import / export for Magento | Ecommerce library

I think that the comments are right for the most part - this probably won't help your regression.

But - to answer how to literally do this; usually what you would do is to use powers of 2 (or 3).

So, for typical "yes/no" where you don't care about the 3rd one, you'd assign things like this:

We could extract the 'vals' from the list output and select those specific elements

Two things: first, you need to pass the transformers or the estimators themselves to the pipeline, not the result of fitting/transforming them (that would give the resultant arrays to the pipeline not the transformers, and it'd fail). Pipeline itself will be fitting/transforming. Second, since you have specific transformations to the specific columns, ColumnTransformer is needed.

Putting these together:

One option is replace and a replacer dict:

The file has to be opened in binary mode.

open(DATA_FILE, 'rb')

You need to set self.theta to be an array, not a scalar (at least in this specific problem).

In your case, (intercepted-augmented) X is a '3 by n' array, so try self.theta = [0, 0, 0] for example. This will correct the specific error 'bool' object has no attribute 'mean'. Still, this will just produce preds as a zero vector; you haven't fit the model yet.

To let you know how I approached the error, I first went to the exact line the error message was pointing to, and put print(preds == y) before the line, and it printed out False. I guess what you expected was a vector of True and Falses. Your y seemed okay; it was a vector (a list to be specific). So I tried print(pred), which showed me a '3 by n' array, which is weird. Now going up from that line, I found out that pred comes from predict_prob(), especially np.dot(X, self.theta). Here, when X is a '3 by n' array and self.theta is a scalar, numpy seems to multiply the scalar to each item in the array and return the array (having the same dimension as the original array), instead of doing matrix multiplication! So you need to explicitly provide self.theta as an array (conforming to the dimension of X).

Hope the answer and the reasoning behind it helped.

As for the red line you mentioned in the comment, I guess it is also because you are not fitting the model. (To see the problem, put print(probs) before plt.countour(...). You'll see an array with 0.5 only.)

So try putting model.fit(X, y) before preds = model.predict(X). (You'll also need to put self.verbose = verbose in the __init__().)

After that, I get the following:

In this case, what you have is a basic question in algebra; Python is simply your implementation language. You have three points, so you need an appropriate function with three parameters.

For a polynomial, you would need a quadratic equation, ax^2 + bx + c.

For a simple exponential, you would need something like a * e^bx + c

You have to choose your equation form; then simply solve it on each of your three points: you have three equations in three variables. You should be able to do this by hand (since you don't want a canned program to do that for you).

In particular, the logistic function is

Multi-collinearity isn't always a hindrance. It depends from data to data. If your model isn't giving you the best results(high accuracy or low loss), you then remove the outliers or highly correlated features to improve it but is everything is hunky-dory, you don't bother about them.

Same goes with polynomial regression. Yes it adds multi-collinearity in your model by introducing x^2, x^3 features into your model.

To overcome that, you can use orthogonal polynomial regression which introduces polynomials that are orthogonal to each other.

But it will still introduce higher degree polynomials which can become unstable at the boundaries of your data space.

To overcome this issue, you can use Regression Splines in which it divides the distribution of the data into separate portions and fit linear or low degree polynomial functions on each of these portions. The points where the division occurs are called Knots. Functions which we can use for modelling each piece/bin are known as Piecewise functions. This function has a constraint , suppose, if it is introducing 3 degree of polynomials or cubic features and then the function should be second-order differentiable. Such a piecewise polynomial of degree m with m-1 continuous derivatives is called a Spline.

You've defined feature_names in terms of the features from a CountVectorizer with the default stop_words=None, but your model in the last bit of code is using a TfidfVectorizer with stop_words='english'. Use instead