Kernel crashes in Logistic Regression model specification

Questions v5
1

Hi,

I am trying to sample from the posterior resulting of this (Logistic Regression) model specification:

lower = 0.01
upper = 1

with pm.Model() as customer_model:

    beta_0 = pm.Uniform('beta_0', lower=lower, upper= upper)
    beta_feature_one = pm.Uniform(
        'beta_feature_one', 
        lower = X_train['feature_one'].min(), 
        upper = X_train['feature_one'].max()
    )
    beta_feature_two= pm.Uniform(
        'beta_feature_two', 
        lower = X_train['feature_two'].min(), 
        upper = X_train['feature_two'].max()
    )
    beta_feature_three = pm.Uniform(
        'beta_feature_three', 
        lower = X_train['feature_three'].min(), 
        upper = X_train['feature_three'].max(),
    )
    beta_feature_four = pm.TruncatedNormal(
        'beta_feature_four', 
        mu = 10, 
        sigma = 44, 
        lower = 0    
    )
    beta_feature_five = pm.Normal(
        'beta_feature_five', 
        mu = 598, 
        sigma = 340
    )
    beta_feature_six = pm.Normal(
        'beta_feature_six', 
        mu = 32180, 
        sigma = 15687
    )
    lin_comb = (
        beta_0 +
        beta_feature_one * X_train['feature_one'] +
        beta_feature_two* X_train['feature_two'] +
        beta_feature_three * X_train['feature_three'] +
        beta_feature_four * X_train['feature_four'] +
        beta_feature_five * X_train['feature_five'] +
        beta_feature_six * X_train['feature_six'] 
    )
    
    p = pm.Deterministic('p', pm.math.sigmoid(lin_comb))

    likelihood = pm.Bernoulli(
        'observed_default',
        p = p,
        observed = y_train
    )
    
    trace = pm.sample()

I am unsure about which values to pass to tune and draws-params in pm.sample() so I decided to keep the default values (after passing other values first). Also, I cannot discern any distribution after plotting selected features, so I have decided to assume a uniform distribution for them. Finally, I am aware of the importance of scaling features first, but decided to run this version of the project without scaling.

This is the error message I get Cannot execute code, session has been disposed. Please try restarting the Kernel. The Kernel crashed while executing code in the the current cell or a previous cell.(...)

I have restarted the Kernel and cleared the notebook prior to running it again (but getting the same error message). I am running the model in pymc5.

What am I missing in this model specification? Could it be that it is not the model itself, but that I need to scale the features first?

Thanks in advance!