Error rate and failure analysis in Agentic Ai - Model Pipeline Trace

Model Pipeline - Error rate and failure analysis

This pipeline shows how a model learns from data, makes mistakes, and how we analyze those mistakes to improve it.

Data Flow - 7 Stages

1Raw Data Input

1000 rows x 10 columns→Collect raw data with features and labels→1000 rows x 10 columns

Feature1=5.2, Feature2=3.1, ..., Label=1

↓

2Data Cleaning

1000 rows x 10 columns→Remove missing values and fix errors→980 rows x 10 columns

Removed 20 rows with missing values

↓

3Feature Engineering

980 rows x 10 columns→Create new features and scale data→980 rows x 12 columns

Added Feature11 = Feature1 / Feature2

↓

4Train/Test Split

980 rows x 12 columns→Split data into training and testing sets→Training: 784 rows x 12 columns, Testing: 196 rows x 12 columns

Training set used for learning, testing set for evaluation

↓

5Model Training

784 rows x 12 columns→Train model to learn patterns→Trained model

Model learns to predict labels from features

↓

6Model Evaluation

196 rows x 12 columns→Predict on test data and calculate error rate→Predictions and error metrics

Error rate = 0.15 (15% wrong predictions)

↓

7Failure Analysis

Predictions and true labels→Analyze wrong predictions to find patterns→Insights on model weaknesses

Most errors occur when Feature3 < 0.5

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, accuracy low
2	0.50	0.72	Loss decreases, accuracy improves
3	0.40	0.80	Model learning well, better predictions
4	0.35	0.85	Loss continues to drop, accuracy rises
5	0.30	0.88	Training converging, good performance

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer (ReLU activation)

Layer 3: Output Layer (Sigmoid activation)

Layer 4: Threshold Decision

Model Quiz - 3 Questions

What does the error rate tell us about the model?

A The percentage of wrong predictions on test data

B The number of features used in training

C The total training time in seconds

D The size of the training dataset

Key Insight

Error rate shows how often the model makes mistakes. By analyzing these mistakes, we can find patterns that help us improve the model's accuracy and reliability.