https://youtu.be/Jb9sxlnhZMo#The Chi-Squared test determines whether there’s a significant association between categorical variables.#It compares the observed frequencies (counts) to the expected frequencies, calculated under the null hypothesis that the variables are independent or that the observed distribution fits a given distribution. #Chi-Squared Test for Independence#This test checks if there’s an association between two categorical variables by […]
Python One-Sample T-Test
One-Sample T-Test Used to compare the mean of a single sample to a known value (usually a population mean). Example 1 Manual Calculation Step 5: Degrees of freedom and p-value Example 2 – Shoes Two Tail Example 3 – Rookie Batting Average One Tail rookie batting average is the same as the population mean (0.250)Rookie […]
Elastic Net Regression
https://youtu.be/xl6KAAVytEk#Elastic Net regression is a type of linear regression technique that combines#the features of both L1 Lasso regression and L2 Ridge regression. #Help prevent overfittting #best used data set many features with some correlated #The combined penalty term is controlled by two hyperparameters:alpha, L1 Ratio #l1_ratio: Another way to specify the balance between L1 and […]
Inverse Matrix
2×2 example 3×3 example properties A^-1 * A = A * A^-1 = I (AB)^−1=B^−1A^−1 (A^T)^−1=(A^−1)^T (kA)^−1=(1/k)(A^−1) (A^−1)^−1=A
Python Identity Matrix
Example 1 Create a 3×3 identity matrix also can use np.eye Example 2 Matrix Multiplication Example 3 Determinant of the Identity Matrix Example 4 Identity Matrix is Its Own Inverse Example 5 Eigenvalues of the Identity Matrix
Python Determinant of a Matrix
2×2 Example 3×3 Example singular matrix identity matrix The determinant of a product of matrices satisfies det(AB)=det(A)⋅det(B) Swapping two rows (or columns) of a matrix multiplies the determinant by − 1 Eigenvalues of A and their product Scale A by a scalar k and compute determinant the shape attribute of a numpy array provides the […]
Ridge Regressor
https://youtu.be/GMF4Td7KtB0#Ridge Regression which is considered #L2 Regularization #helps with overfitting in linear regression models #keeping the coefficients small # lead to a model that is less prone to overfitting #balance between fitting the data and keeping the coefficients small #more robust and stable models, particularly when dealing with datasets that have highly correlated predictor variables […]
Stacking Regressor
SEE ALL NULL VLAUES voting classifier hyperparamater tuning