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fix simple typos #10

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fix simple typos #10

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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -80,7 +80,7 @@ Array of regression coefficients, dimensions `targets x features`. If an interce
the first feature.

#### `model.betas_and_scores`
Array of regression coefficients, followed by prediction scores on the fitted data, dimensions `targets x (feature + 1)`. If an intercept was fit, it will be the the first feature.
Array of regression coefficients, followed by prediction scores on the fitted data, dimensions `targets x (feature + 1)`. If an intercept was fit, it will be the first feature.

#### `model.models`
Array of individual fitted models, dimensions `1 x targets`.
Expand Down Expand Up @@ -120,7 +120,7 @@ Linear regression through ordinary least squares as implemented in scikit-learn'
#### `CustomRegression(algorithm)`
Use a custom regression algorithm in a mass regression analysis. The provided `algorithm` should operate on single response variables, and must conform to the `scikit-learn` API as follows
- Must implement a `.fit(X, Y)` method that takes a design matrix (`samples x features`) and a response vector and returns an object representing the fitted model.
- The returned fitted model must must have attributes `.coef_` and `.intercept_` that hold the results of the the fit (`.coef_` having dimensions `1 x features` and `.intercept_` being a scalar).
- The returned fitted model must have attributes `.coef_` and `.intercept_` that hold the results of the fit (`.coef_` having dimensions `1 x features` and `.intercept_` being a scalar).
- The returned fitted model must also have methods `.predict(X)` and `.score(X, y)` (`X` having dimensions `new samples x features` and `y` having dimensions `1 x new samples`). The former should return a vector of predictions (dimensions `1 x new samples`) and the former should return a scalar score (likely r-squared).

This allows you to define an algorithm in [`scikit-learn`](https://github.com/scikit-learn/scikit-learn) and then wrap it for mass fitting, for example
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