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How to handle imbalanced datasets effectively in classification problems?

I’m working on a classification problem where one class heavily outweighs the others (around 90:10 ratio). My model is achieving high accuracy, but it’s clearly biased toward the majority class. Here’s a simplified version: from sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import classification_report X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model =(Read More)

I’m working on a classification problem where one class heavily outweighs the others (around 90:10 ratio). My model is achieving high accuracy, but it’s clearly biased toward the majority class.

Here’s a simplified version:

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

Accuracy looks good, but recall and precision for the minority class are poor.

What I want to understand:

What are the best techniques to handle imbalance (SMOTE, class weights, etc.)?
When should I prefer resampling vs adjusting model parameters?
Which evaluation metrics should I focus on in such cases?

Would appreciate practical advice based on real-world experience.

0 1 116 1 month ago

Subscriber

Naomi Teng

March 30, 2026

How to handle dynamic schema changes in Alteryx workflows?

I’m working on an Alteryx workflow where the input data schema changes frequently (new columns get added, some get removed, and column order varies). This is causing issues with tools like Select, Join, and Union, where the workflow breaks if expected fields are missing or renamed. For example, I’m reading multiple files: Input Data →(Read More)

I’m working on an Alteryx workflow where the input data schema changes frequently (new columns get added, some get removed, and column order varies).

This is causing issues with tools like Select, Join, and Union, where the workflow breaks if expected fields are missing or renamed.

For example, I’m reading multiple files:

Input Data → Select → Join → Output

But when a new column appears in one file or a column is missing in another, the workflow fails or produces inconsistent output.

What I’ve tried:

Using Auto Config by Name in Union
Dynamic Rename tool
Select with “Unknown” fields

Still facing issues with joins and downstream tools.

My questions:

What’s the best way to make Alteryx workflows resilient to schema changes?
Are there recommended patterns or tools (Dynamic Input, Field Info, etc.) for handling this?
How do you ensure joins don’t break when fields are inconsistent?

Would appreciate any best practices or real-world approaches.

0 2 123 1 month ago

Subscriber

Naomi Teng

March 29, 2026

Why does my deep learning model do well on training data but poorly on validation?

I’m new to deep learning and currently training my first few neural network models. During training, the accuracy keeps improving and the loss goes down nicely. But when I evaluate the model on validation data, the performance drops a lot. This feels confusing because the training results look “good” at first glance. I’m trying to(Read More)

I’m new to deep learning and currently training my first few neural network models.

During training, the accuracy keeps improving and the loss goes down nicely. But when I evaluate the model on validation data, the performance drops a lot. This feels confusing because the training results look “good” at first glance.

I’m trying to understand this at a conceptual level, not just apply fixes blindly.

Some things I’m wondering about: