Rules for Classification and Regression
When you use rules in machine learning, you can target either classification or regression problems. In classification, rules are designed to predict discrete categories or labels, such as "spam" or "not spam", "high risk" or "low risk". For regression, rules are used to predict a continuous numerical value, such as a house price or the temperature for the next day. Imagine a medical dataset: a rule for classification might state, If age > 60 and blood pressure > 140, then risk = 'high', while a regression rule could be, If age > 60 and blood pressure > 140, then predicted cost = 5000. In both cases, the rule structure is similar, but the outcome type and prediction logic differ.
123456789101112131415161718192021222324252627282930313233343536373839# Example: Simple rule-based classifier and regressor in Python import pandas as pd # Sample data for classification data_class = pd.DataFrame({ "age": [22, 45, 61, 38, 70], "bp": [120, 135, 150, 142, 160], "label": ["low", "low", "high", "low", "high"] }) # Rule-based classifier: if age > 60 and bp > 140, predict "high", else "low" def rule_based_classifier(row): if row["age"] > 60 and row["bp"] > 140: return "high" else: return "low" data_class["predicted_label"] = data_class.apply(rule_based_classifier, axis=1) print("Classification predictions:") print(data_class[["age", "bp", "predicted_label"]]) # Sample data for regression data_reg = pd.DataFrame({ "rooms": [2, 3, 4, 5, 6], "sqft": [800, 1200, 1500, 2000, 2500], "price": [150, 200, 250, 320, 400] }) # Rule-based regressor: if rooms >= 5 and sqft > 1800, predict 350, else 200 def rule_based_regressor(row): if row["rooms"] >= 5 and row["sqft"] > 1800: return 350 else: return 200 data_reg["predicted_price"] = data_reg.apply(rule_based_regressor, axis=1) print("\nRegression predictions:") print(data_reg[["rooms", "sqft", "predicted_price"]])
When you compare the rule structures in classification and regression, you will notice that both use logical conditions to trigger a prediction. However, the prediction logic is different: classification rules assign a category (such as "high" or "low"), while regression rules assign a numeric value (such as 350). In the code above, the classifier checks if both age and blood pressure cross certain thresholds and then outputs a label; the regressor checks if both the number of rooms and square footage are above certain values and then outputs a price estimate. This means that while the rule's condition part ("if ...") can look similar, the action part ("then ...") is tailored to the problem type—discrete category for classification, continuous value for regression.
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When you use rules in machine learning, you can target either classification or regression problems. In classification, rules are designed to predict discrete categories or labels, such as "spam" or "not spam", "high risk" or "low risk". For regression, rules are used to predict a continuous numerical value, such as a house price or the temperature for the next day. Imagine a medical dataset: a rule for classification might state, If age > 60 and blood pressure > 140, then risk = 'high', while a regression rule could be, If age > 60 and blood pressure > 140, then predicted cost = 5000. In both cases, the rule structure is similar, but the outcome type and prediction logic differ.
123456789101112131415161718192021222324252627282930313233343536373839# Example: Simple rule-based classifier and regressor in Python import pandas as pd # Sample data for classification data_class = pd.DataFrame({ "age": [22, 45, 61, 38, 70], "bp": [120, 135, 150, 142, 160], "label": ["low", "low", "high", "low", "high"] }) # Rule-based classifier: if age > 60 and bp > 140, predict "high", else "low" def rule_based_classifier(row): if row["age"] > 60 and row["bp"] > 140: return "high" else: return "low" data_class["predicted_label"] = data_class.apply(rule_based_classifier, axis=1) print("Classification predictions:") print(data_class[["age", "bp", "predicted_label"]]) # Sample data for regression data_reg = pd.DataFrame({ "rooms": [2, 3, 4, 5, 6], "sqft": [800, 1200, 1500, 2000, 2500], "price": [150, 200, 250, 320, 400] }) # Rule-based regressor: if rooms >= 5 and sqft > 1800, predict 350, else 200 def rule_based_regressor(row): if row["rooms"] >= 5 and row["sqft"] > 1800: return 350 else: return 200 data_reg["predicted_price"] = data_reg.apply(rule_based_regressor, axis=1) print("\nRegression predictions:") print(data_reg[["rooms", "sqft", "predicted_price"]])
When you compare the rule structures in classification and regression, you will notice that both use logical conditions to trigger a prediction. However, the prediction logic is different: classification rules assign a category (such as "high" or "low"), while regression rules assign a numeric value (such as 350). In the code above, the classifier checks if both age and blood pressure cross certain thresholds and then outputs a label; the regressor checks if both the number of rooms and square footage are above certain values and then outputs a price estimate. This means that while the rule's condition part ("if ...") can look similar, the action part ("then ...") is tailored to the problem type—discrete category for classification, continuous value for regression.
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