Naive Bayes classifier

We have calculated priors and likelihoods for our data with Laplace smoothing. Apply MAP decision rule to update the label for the entry$(\text{Precipitation = No, Temperature = 0-5 °C, Wind = Breeze})$.

$$\begin{aligned} P(\text{Snowboarding = No}) &= \frac{1}{2} \\ P(\text{Snowboarding = Yes}) &= \frac{1}{2}\\ P(\text{Precipitation = No} | \text{Snowboarding = No}) &= \frac{3}{7}\\ P(\text{Precipitation = No} | \text{Snowboarding = Yes}) &= \frac{4}{7}\\ P(\text{Temperature = 0-5 °C} | \text{Snowboarding = No}) &= \frac{3}{7}\\ P(\text{Temperature = 0-5 °C} | \text{Snowboarding = Yes}) &= \frac{3}{7}\\ P(\text{Wind = Breeze} | \text{Snowboarding = No}) &= \frac{3}{7} \\ P(\text{Wind = Breeze} | \text{Snowboarding = Yes}) &= \frac{2}{7} \end{aligned}$$