Bayesian Machine Learning for Optimization in Python/

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Techniques to Estimate Model Parameters

Techniques to Estimate Model Parameters

Learn about different techniques to estimate model parameters using probability.

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Maximum likelihood estimation (MLE), maximum a posteriori estimation (MAP), and Bayesian inference are all techniques used in machine learning to estimate the parameters of model-given data.

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Prior, MLE, and posterior estimations of a normal distributions
Prior, MLE, and posterior estimations of a normal distributions

Maximum likelihood estimation

Maximum likelihood estimation (MLE) is a method for estimating the parameters of a probability distribution given a dataset. It involves finding the set of parameters that maximizes the likelihood of the observed data. MLE is a simple and widely used method in machine learning, particularly in supervised learning. It is generally easier to compute and understand. However, it doesn’t take into account any prior knowledge or belief about the parameters.

Example: Bug prediction

One example of using MLE in software engineering is in the field of bug prediction. Bug prediction is a technique used to predict the likelihood of a code having a bug. In this case, the goal is to estimate the probability of a bug given certain code features, such as the number of lines of code, the number of comments, and the number of changes.

Here’s how this can be done in Python:

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main.py
software_data_file.csv
from sklearn.linear_model import LogisticRegression
import pandas as pd
# Create a training dataset
code_data = pd.read_csv("software_data_file.csv")
# Extract the features
X = code_data[["lines_of_code", "comments", "changes"]]
# Create the labels
y = code_data["bug"]
# Create and train the model
model = LogisticRegression(solver='lbfgs')
model.fit(X, y)
# Test the model with new code data
new_code = [[100, 20, 5]] #Change these values and see effect on the probability
prediction = model.predict_proba(new_code)
prob_a=prediction[0][1]*100
print(f"The likelihood of having a bug in new code is {round(prob_a,2)}%")

In this code, a ...

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