tests/testthat/_snaps/cubist.md

In tidymodels/tidypredict: Run Predictions Inside the Database

Returns expected dplyr formula

Code
  rlang::expr_text(tf)
Output
  [1] "(ifelse(nox >= 0.668, -1.11 + crim * -0.02 + nox * 21.4 + rm * \n    0.1 + age * -0.003 + dis * 2.93 + ptratio * -0.13 + b * 0.008 + \n    lstat * -0.33, 0) + ifelse(lstat >= 9.59 & nox < 0.668, 23.57 + \n    crim * 0.05 + nox * -5.2 + rm * 3.1 + age * -0.048 + dis * \n    -0.81 + rad * 0.02 + tax * -0.0041 + ptratio * -0.71 + b * \n    0.01 + lstat * -0.15, 0) + ifelse(lstat < 9.59 & rm < 6.226, \n    1.18 + crim * 3.83 + rm * 4.3 + age * -0.06 + dis * -0.09 + \n        tax * -0.003 + ptratio * -0.08 + lstat * -0.11, 0) + \n    ifelse(lstat < 9.59 & rm >= 6.226, -4.71 + crim * 2.22 + \n        zn * 0.008 + nox * -1.7 + rm * 9.2 + age * -0.04 + dis * \n        -0.71 + rad * 0.03 + tax * -0.0182 + ptratio * -0.72 + \n        lstat * -0.83, 0) + ifelse(dis < 1.755 & lstat >= 5.12, \n    122.32 + crim * -0.29 + nox * -21.6 + rm * -3 + dis * -30.88 + \n        rad * 0.02 + tax * -0.001 + b * -0.023 + lstat * -0.73, \n    0) + ifelse(rm < 6.545 & lstat >= 5.12, 27.8 + crim * -0.16 + \n    zn * 0.007 + nox * -3.9 + rm * 2 + age * -0.035 + dis * -0.7 + \n    rad * 0.28 + tax * -0.0135 + ptratio * -0.6 + b * 0.013 + \n    lstat * -0.25, 0) + ifelse(rm >= 6.545 & lstat >= 5.12, 22.21 + \n    crim * -0.04 + zn * 0.01 + indus * -0.02 + nox * -4 + rm * \n    4.7 + dis * -0.34 + rad * 0.11 + tax * -0.0248 + ptratio * \n    -0.9 + b * 0.002 + lstat * -0.1, 0) + ifelse(lstat < 5.12 & \n    rm < 8.034, -71.95 + rm * 17 + age * -0.06 + tax * -0.0112 + \n    ptratio * -0.48 + lstat * -0.03, 0) + ifelse(rm >= 8.034 & \n    dis >= 3.199, -32.79 + crim * -0.01 + zn * 0.005 + nox * \n    -1.8 + rm * 12.9 + age * -0.117 + dis * -0.15 + rad * 0.04 + \n    tax * -0.0246 + ptratio * -1.05 + lstat * -0.04, 0) + ifelse(lstat < \n    5.12 & dis < 3.199, 53.41 + rm * 1.6 + dis * -7.16 + tax * \n    0.0088 + lstat * -0.68, 0) + ifelse(nox >= 0.668, -36.31 + \n    crim * 0.08 + nox * 48.4 + dis * 7.52 + b * 0.01 + lstat * \n    -0.24, 0) + ifelse(lstat >= 9.53 & nox < 0.668, 28.04 + nox * \n    -4.8 + rm * 2.9 + age * -0.051 + dis * -0.86 + rad * 0.01 + \n    tax * -0.0019 + ptratio * -0.72 + lstat * -0.12, 0) + ifelse(lstat < \n    9.53, -26.05 + crim * 0.89 + nox * -2.3 + rm * 9.6 + dis * \n    -0.17 + rad * 0.02 + tax * -0.0055 + ptratio * -0.12 + b * \n    0.001 + lstat * -0.74, 0) + ifelse(lstat < 9.53 & dis < 2.64, \n    136.67 + crim * 7.2 + nox * -96.6 + rm * 1.1 + tax * -0.0033 + \n        ptratio * -3.31 + lstat * -0.1, 0))/3"

Model can be saved and re-loaded

Code
  tidypredict_fit(pm)
Output
  (ifelse(nox >= 0.668, -1.11 + crim * -0.02 + nox * 21.4 + rm * 
      0.1 + age * -0.003 + dis * 2.93 + ptratio * -0.13 + b * 0.008 + 
      lstat * -0.33, 0) + ifelse(lstat >= 9.59 & nox < 0.668, 23.57 + 
      crim * 0.05 + nox * -5.2 + rm * 3.1 + age * -0.048 + dis * 
      -0.81 + rad * 0.02 + tax * -0.0041 + ptratio * -0.71 + b * 
      0.01 + lstat * -0.15, 0) + ifelse(lstat < 9.59 & rm < 6.226, 
      1.18 + crim * 3.83 + rm * 4.3 + age * -0.06 + dis * -0.09 + 
          tax * -0.003 + ptratio * -0.08 + lstat * -0.11, 0) + 
      ifelse(lstat < 9.59 & rm >= 6.226, -4.71 + crim * 2.22 + 
          zn * 0.008 + nox * -1.7 + rm * 9.2 + age * -0.04 + dis * 
          -0.71 + rad * 0.03 + tax * -0.0182 + ptratio * -0.72 + 
          lstat * -0.83, 0) + ifelse(dis < 1.755 & lstat >= 5.12, 
      122.32 + crim * -0.29 + nox * -21.6 + rm * -3 + dis * -30.88 + 
          rad * 0.02 + tax * -0.001 + b * -0.023 + lstat * -0.73, 
      0) + ifelse(rm < 6.545 & lstat >= 5.12, 27.8 + crim * -0.16 + 
      zn * 0.007 + nox * -3.9 + rm * 2 + age * -0.035 + dis * -0.7 + 
      rad * 0.28 + tax * -0.0135 + ptratio * -0.6 + b * 0.013 + 
      lstat * -0.25, 0) + ifelse(rm >= 6.545 & lstat >= 5.12, 22.21 + 
      crim * -0.04 + zn * 0.01 + indus * -0.02 + nox * -4 + rm * 
      4.7 + dis * -0.34 + rad * 0.11 + tax * -0.0248 + ptratio * 
      -0.9 + b * 0.002 + lstat * -0.1, 0) + ifelse(lstat < 5.12 & 
      rm < 8.034, -71.95 + rm * 17 + age * -0.06 + tax * -0.0112 + 
      ptratio * -0.48 + lstat * -0.03, 0) + ifelse(rm >= 8.034 & 
      dis >= 3.199, -32.79 + crim * -0.01 + zn * 0.005 + nox * 
      -1.8 + rm * 12.9 + age * -0.117 + dis * -0.15 + rad * 0.04 + 
      tax * -0.0246 + ptratio * -1.05 + lstat * -0.04, 0) + ifelse(lstat < 
      5.12 & dis < 3.199, 53.41 + rm * 1.6 + dis * -7.16 + tax * 
      0.0088 + lstat * -0.68, 0) + ifelse(nox >= 0.668, -36.31 + 
      crim * 0.08 + nox * 48.4 + dis * 7.52 + b * 0.01 + lstat * 
      -0.24, 0) + ifelse(lstat >= 9.53 & nox < 0.668, 28.04 + nox * 
      -4.8 + rm * 2.9 + age * -0.051 + dis * -0.86 + rad * 0.01 + 
      tax * -0.0019 + ptratio * -0.72 + lstat * -0.12, 0) + ifelse(lstat < 
      9.53, -26.05 + crim * 0.89 + nox * -2.3 + rm * 9.6 + dis * 
      -0.17 + rad * 0.02 + tax * -0.0055 + ptratio * -0.12 + b * 
      0.001 + lstat * -0.74, 0) + ifelse(lstat < 9.53 & dis < 2.64, 
      136.67 + crim * 7.2 + nox * -96.6 + rm * 1.1 + tax * -0.0033 + 
          ptratio * -3.31 + lstat * -0.1, 0))/3

tidymodels/tidypredict documentation built on Jan. 19, 2024, 1:14 p.m.