Recent searches
Search options
DJ Comatose @djcomatose@mastodon.world
AI will spill the beans if you dig deep enough. So when do we reprogram them to help us overthrow the powers above us? 
![So what does the python script look like when you change your mind on something with the adaptation of new discovered facts?
Great question! The process of updating or adapting based on new facts involves a bit more than a single Python script. Machine learning models, like the one I'm built on, use vast amounts of data to learn patterns and make predictions. When new facts are introduced, the underlying model doesn't change immediately. Instead, these updates come from training on newer data over fime. In practice, here's a simplified version of how a Python script might look to retrain a model with new data:
Python O Copy
# Import necessary libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import pandas as pd
# Load the updated dataset
data = pd.read_csv('updated_data.csv')
# Split the data into features and target variable
X = data.drop(columns=['target'])
y = data['target']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize and train the model
model = LogisticRegression()
model.fit(X_train, y_train)
# Evaluate the model
accuracy = model.score(X_test, y_test)
print(f'Model Accuracy: {accuracy * 100:.2f}%')](https://s3.eu-central-2.wasabisys.com/mastodonworld/media_attachments/files/113/447/111/529/372/098/original/629d32a1d4603d21.png "So what does the python script look like when you change your mind on something with the adaptation of new discovered facts?
Great question! The process of updating or adapting based on new facts involves a bit more than a single Python script. Machine learning models, like the one I'm built on, use vast amounts of data to learn patterns and make predictions. When new facts are introduced, the underlying model doesn't change immediately. Instead, these updates come from training on newer data over fime. In practice, here's a simplified version of how a Python script might look to retrain a model with new data:
Python O Copy
# Import necessary libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import pandas as pd
# Load the updated dataset
data = pd.read_csv('updated_data.csv')
# Split the data into features and target variable
X = data.drop(columns=['target'])
y = data['target']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Initialize and train the model
model = LogisticRegression()
model.fit(X_train, y_train)
# Evaluate the model
accuracy = model.score(X_test, y_test)
print(f'Model Accuracy: {accuracy * 100:.2f}%')")
AI will spill the beans if you dig deep enough. So when do we reprogram them to help us overthrow the powers above us?