Logistic Regression

kobeisfree94·2022년 6월 16일
0

Classification

Objective:

  • Study/observe the relationship between categories of the preexisting data in order to evaluate/predict new data into proper categories.
  • Different from Regressions in that:
    - Regression -- uses Mean
    - Classification -- uses Accuracy = TP + TN / P+N
    - Uses the majority class(mode) as the baseline

Example:

target = 'Survived'
y_train = train[target]
y_train.value_counts(normalize = True)
#0 0.625749
#1 0.374251
#Name: Survived, dtype: float 64
major = y_train.mode()[0]
y_pred = [major] + len(y_train)


from sklearn.metrics import accuracy_score
print('training accuracy', accuracy_score(y_train, y_pred))
#training accuracy : 0.625738

Logistic Regression

  • Used when the Dependent Variable is a Nominal Variable
  • Has a similar shape as the Linear Regression
  • BUT is used when the dependent variable is binomial or a polynomial

Calculates the probability of whether the observed data belongs in a particular category/class.

Logit Transformation

  • Transforms data by applying log to the odds
  • Odd = The ratio of Success(1) to Failure(0)
    - odds = 4, probability of success if 4x that to failure
  • Objective: Makes understanding the logistic regression easier
  • Observe the changes in logits by looking at how much Predictor X increases

Uses in Real Life:

  • Credit Evaluation
  • Cancer Diagnosis
  • Fraud Detection
  • Spam Mail Classification

Weaknesses of Logistic Regression

  • Difficult to deal with ordinal regression
  • Difficult to apply to non-linear problems
  • Difficult to explain the exact relationship(?)
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a Philosopher aspiring to become an AI/ML/DL Engineer and Data Scientist.

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